siRNA targeting cystic fibrosis transmembrane conductance regulator (CFTR)

ABSTRACT

Efficient sequence specific gene silencing is possible through the use of siRNA technology. By selecting particular siRNAs by rational design, one can maximize the generation of an effective gene silencing reagent, as well as methods for silencing genes. Methods, compositions, and kits generated through rational design of siRNAs are disclosed including those directed to CFTR.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Ser. No. 10/714,333,filed Nov. 14, 2003, which claims the benefit of U.S. ProvisionalApplication No. 60/426,137, filed Nov. 14, 2002, and also claims thebenefit of U.S. Provisional Application No. 60/502,050, filed Sep. 10,2003; this application is also a continuation-in-part of U.S. Ser. No.10/940,892, filed Sep. 14, 2004, which is a continuation of PCTApplication No. PCT/US04/14885, international filing date May 12, 2004.The disclosures of the priority applications, including the sequencelistings and tables submitted in electronic form in lieu of paper, areincorporated by reference into the instant specification.

Sequence Listing

The sequence listing for this application has been submitted inaccordance with 37 CFR § 1.52(e) and 37 CFR § 1.821 on CD-ROM in lieu ofpaper on a disk containing the sequence listing file entitled“DHARMA_(—)2100-US66_CRF.txt” created Jun. 14, 2007, 112 kb. Applicantshereby incorporate by reference the sequence listing provided on CD-ROMin lieu of paper into the instant specification.

FIELD OF INVENTION

The present invention relates to RNA interference (“RNAi”).

BACKGROUND OF THE INVENTION

Relatively recently, researchers observed that double stranded RNA(“dsRNA”) could be used to inhibit protein expression. This ability tosilence a gene has broad potential for treating human diseases, and manyresearchers and commercial entities are currently investing considerableresources in developing therapies based on this technology.

Double stranded RNA induced gene silencing can occur on at least threedifferent levels: (i) transcription inactivation, which refers to RNAguided DNA or histone methylation; (ii) siRNA induced mRNA degradation;and (iii) mRNA induced transcriptional attenuation.

It is generally considered that the major mechanism of RNA inducedsilencing (RNA interference, or RNAi) in mammalian cells is mRNAdegradation. Initial attempts to use RNAi in mammalian cells focused onthe use of long strands of dsRNA. However, these attempts to induce RNAimet with limited success, due in part to the induction of the interferonresponse, which results in a general, as opposed to a target-specific,inhibition of protein synthesis. Thus, long dsRNA is not a viable optionfor RNAi in mammalian systems.

More recently it has been shown that when short (18-30 bp) RNA duplexesare introduced into mammalian cells in culture, sequence-specificinhibition of target mRNA can be realized without inducing an interferonresponse. Certain of these short dsRNAs, referred to as small inhibitoryRNAs (“siRNAs”), can act catalytically at sub-molar concentrations tocleave greater than 95% of the target mRNA in the cell. A description ofthe mechanisms for siRNA activity, as well as some of its applicationsare described in Provost et al. (2002) Ribonuclease Activity and RNABinding of Recombinant Human Dicer, EMBO J. 21(21): 5864-5874; Tabara etal. (2002) The dsRNA Binding Protein RDE-4 Interacts with RDE-1, DCR-1and a DexH-box Helicase to Direct RNAi in C. elegans, Cell109(7):861-71; Ketting et al. (2002) Dicer Functions in RNA Interferenceand in Synthesis of Small RNA Involved in Developmental Timing in C.elegans; Martinez et al., Single-Stranded Antisense siRNAs Guide TargetRNA Cleavage in RNAi, Cell 110(5):563; Hutvagner & Zamore (2002) AmicroRNA in a multiple-turnover RNAi enzyme complex, Science 297:2056.

From a mechanistic perspective, introduction of long double stranded RNAinto plants and invertebrate cells is broken down into siRNA by a TypeIII endonuclease known as Dicer. Sharp, RNA interference—2001, GenesDev. 2001, 15:485. Dicer, a ribonuclease-III-like enzyme, processes thedsRNA into 19-23 base pair short interfering RNAs with characteristictwo base 3′ overhangs. Bernstein, Caudy, Hammond, & Hannon (2001) Rolefor a bidentate ribonuclease in the initiation step of RNA interference,Nature 409:363. The siRNAs are then incorporated into an RNA-inducedsilencing complex (RISC) where one or more helicases unwind the siRNAduplex, enabling the complementary antisense strand to guide targetrecognition. Nykanen, Haley, & Zamore (2001) ATP requirements and smallinterfering RNA structure in the RNA interference pathway, Cell 107:309.Upon binding to the appropriate target mRNA, one or more endonucleaseswithin the RISC cleaves the target to induce silencing. Elbashir,Lendeckel, & Tuschl (2001) RNA interference is mediated by 21- and22-nucleotide RNAs, Genes Dev. 15:188, FIG. 1.

The interference effect can be long lasting and may be detectable aftermany cell divisions. Moreover, RNAi exhibits sequence specificity.Kisielow, M. et al. (2002) Isoform-specific knockdown and expression ofadaptor protein ShcA using small interfering RNA, J. Biochem. 363:1-5.Thus, the RNAi machinery can specifically knock down one type oftranscript, while not affecting closely related mRNA. These propertiesmake siRNA a potentially valuable tool for inhibiting gene expressionand studying gene function and drug target validation. Moreover, siRNAsare potentially useful as therapeutic agents against: (1) diseases thatare caused by over-expression or misexpression of genes; and (2)diseases brought about by expression of genes that contain mutations.

Successful siRNA-dependent gene silencing depends on a number offactors. One of the most contentious issues in RNAi is the question ofthe necessity of siRNA design, i.e., considering the sequence of thesiRNA used. Early work in C. elegans and plants circumvented the issueof design by introducing long dsRNA (see, for instance, Fire, A. et al.(1998) Nature 391:806-811). In this primitive organism, long dsRNAmolecules are cleaved into siRNA by Dicer, thus generating a diversepopulation of duplexes that can potentially cover the entire transcript.While some fraction of these molecules are non-functional (i.e., inducelittle or no silencing) one or more have the potential to be highlyfunctional, thereby silencing the gene of interest and alleviating theneed for siRNA design. Unfortunately, due to the interferon response,this same approach is unavailable for mammalian systems. While thiseffect can be circumvented by bypassing the Dicer cleavage step anddirectly introducing siRNA, this tactic carries with it the risk thatthe chosen siRNA sequence may be non-functional or semi-functional.

A number of researches have expressed the view that siRNA design is nota crucial element of RNAi. On the other hand, others in the field havebegun to explore the possibility that RNAi can be made more efficient bypaying attention to the design of the siRNA. Unfortunately, none of thereported methods have provided a satisfactory scheme for reliablyselecting siRNA with acceptable levels of functionality. Accordingly,there is a need to develop rational criteria by which to select siRNAwith an acceptable level of functionality, and to identify siRNA thathave this improved level of functionality, as well as to identify siRNAsthat are hyperfunctional.

SUMMARY OF THE INVENTION

The present invention is directed to increasing the efficiency of RNAi,particularly in mammalian systems. Accordingly, the present inventionprovides kits, siRNAs and methods for increasing siRNA efficacy.

According to a first embodiment, the present invention provides a kitfor gene silencing, wherein said kit is comprised of a pool of at leasttwo siRNA duplexes, each of which is comprised of a sequence that iscomplementary to a portion of the sequence of one or more targetmessenger RNA, and each of which is selected using non-target specificcriteria.

According to a second embodiment, the present invention provides amethod for selecting an siRNA, said method comprising applying selectioncriteria to a set of potential siRNA that comprise 18-30 base pairs,wherein said selection criteria are non-target specific criteria, andsaid set comprises at least two siRNAs and each of said at least twosiRNAs contains a sequence that is at least substantially complementaryto a target gene; and determining the relative functionality of the atleast two siRNAs.

According to a third embodiment, the present invention also provides amethod for selecting an siRNA wherein said selection criteria areembodied in a formula comprising:

$\begin{matrix}{{{\left( {- 14} \right)*G_{13}} - {13*A_{1}} - {12*U_{7}} - {11*U_{2}} - {10*A_{11}} - {10*U_{4}} - {10*C_{3}} - {10*C_{5}} - {10*C_{6}} - {9*A_{10}} - {9*U_{9}} - {9*C_{18}} - {8*G_{10}} - {7*U_{1}} - {7*U_{16}} - {7*C_{17}} - {7*C_{19}} + {7*U_{17}} + {8*A_{2}} + {8*A_{4}} + {8*A_{5}} + {8*C_{4}} + {9*G_{8}} + {10*A_{7}} + {10*U_{18}} + {11*A_{19}} + {11*C_{9}} + {15*G_{1}} + {18*A_{3}} + {19*U_{10}} - {Tm} - {3*\left( {GC}_{total} \right)} - {6*\left( {GC}_{15\text{-}19} \right)} - {30*X}};{or}} & {{Formula}\mspace{14mu} {VIII}} \\{{{\left( {- 8} \right)*A\; 1} + {\left( {- 1} \right)*A\; 2} + {(12)*A\; 3} + {(7)*A\; 4} + {(18)*A\; 5} + {(12)*A\; 6} + {(19)*A\; 7} + {(6)*A\; 8} + {\left( {- 4} \right)*A\; 9} + {\left( {- 5} \right)*A\; 10} + {\left( {- 2} \right)*A\; 11} + {\left( {- 5} \right)*A\; 12} + {(17)*A\; 13} + {\left( {- 3} \right)*A\; 14} + {(4)*A\; 15} + {(2)*A\; 16} + {(8)*A\; 17} + {(11)*A\; 18} + {(30)*A\; 19} + {\left( {- 13} \right)*U\; 1} + {\left( {- 10} \right)*U\; 2} + {(2)*U\; 3} + {\left( {- 2} \right)*U\; 4} + {\left( {- 5} \right)*U\; 5} + {(5)*U\; 6} + {\left( {- 2} \right)*U\; 7} + {\left( {- 10} \right)*U\; 8} + {\left( {- 5} \right)*U\; 9} + {(15)*U\; 10} + {\left( {- 1} \right)*U\; 11} + {(0)*U\; 12} + {(10)*U\; 13} + {\left( {- 9} \right)*U\; 14} + {\left( {- 13} \right)*U\; 15} + {\left( {- 10} \right)*U\; 16} + {(3)*U\; 17} + {(9)*U\; 18} + {(9)*U\; 19} + {(7)*C\; 1} + {(3)*C\; 2} + {\left( {- 21} \right)*C\; 3} + {(5)*C\; 4} + {\left( {- 9} \right)*C\; 5} + {\left( {- 20} \right)*C\; 6} + {\left( {- 18} \right)*C\; 7} + {\left( {- 5} \right)*C\; 8} + {(5)*C\; 9} + {(1)*C\; 10} + {(2)*C\; 11} + {\left( {- 5} \right)*C\; 12} + {\left( {- 3} \right)*C\; 13} + {\left( {- 6} \right)*C\; 14} + {\left( {- 2} \right)*C\; 15} + {\left( {- 5} \right)*C\; 16} + {\left( {- 3} \right)*C\; 17} + {\left( {- 12} \right)*C\; 18} + {\left( {- 18} \right)*C\; 19} + {(14)*G\; 1} + {(8)*G\; 2} + {(7)*G\; 3} + {\left( {- 10} \right)*G\; 4} + {\left( {- 4} \right)*G\; 5} + {(2)*G\; 6} + {(1)*G\; 7} + {(9)*G\; 8} + {(5)*G\; 9} + {\left( {- 11} \right)*G\; 10} + {(1)*G\; 11} + {(9)*G\; 12} + {\left( {- 24} \right)*G\; 13} + {(18)*G\; 14} + {(11)*G\; 15} + {(13)*G\; 16} + {\left( {- 7} \right)*G\; 17} + {\left( {- 9} \right)*G\; 18} + {\left( {- 22} \right)*G\; 19} + {6*\left( {{{number}\mspace{14mu} {of}\mspace{14mu} A} + {U\mspace{14mu} {in}\mspace{14mu} {position}\mspace{14mu} 15\text{-}19}} \right)} - {3*\left( {{{number}\mspace{14mu} {of}{\mspace{11mu} \;}G} + {C\mspace{14mu} {in}\mspace{14mu} {whole}\mspace{14mu} {siRNA}}} \right)}},} & {{Formula}\mspace{14mu} X}\end{matrix}$

wherein position numbering begins at the 5′-most position of a sensestrand, andA₁=1 if A is the base at position 1 of the sense strand, otherwise itsvalue is 0;A₂=1 if A is the base at position 2 of the sense strand, otherwise itsvalue is 0;A₃=1 if A is the base at position 3 of the sense strand, otherwise itsvalue is 0;A₄=1 if A is the base at position 4 of the sense strand, otherwise itsvalue is 0;A₅=1 if A is the base at position 5 of the sense strand, otherwise itsvalue is 0;A₆=1 if A is the base at position 6 of the sense strand, otherwise itsvalue is 0;A₇=1 if A is the base at position 7 of the sense strand, otherwise itsvalue is 0;A₁₀=1 if A is the base at position 10 of the sense strand, otherwise itsvalue is 0;A₁₁=1 if A is the base at position 11 of the sense strand, otherwise itsvalue is 0;A₁₃=1 if A is the base at position 13 of the sense strand, otherwise itsvalue is 0;A₁₉=1 if A is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;C₃=1 if C is the base at position 3 of the sense strand, otherwise itsvalue is 0;C₄=1 if C is the base at position 4 of the sense strand, otherwise itsvalue is 0;C₅=1 if C is the base at position 5 of the sense strand, otherwise itsvalue is 0;C₆=1 if C is the base at position 6 of the sense strand, otherwise itsvalue is 0;C₇=1 if C is the base at position 7 of the sense strand, otherwise itsvalue is 0;C₉=1 if C is the base at position 9 of the sense strand, otherwise itsvalue is 0;C₁₇=1 if C is the base at position 17 of the sense strand, otherwise itsvalue is 0;C₁₈=1 if C is the base at position 18 of the sense strand, otherwise itsvalue is 0;C₁₉=1 if C is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;G₁=1 if G is the base at position 1 on the sense strand, otherwise itsvalue is 0;G₂=1 if G is the base at position 2 of the sense strand, otherwise itsvalue is 0;G₈=1 if G is the base at position 8 on the sense strand, otherwise itsvalue is 0;G₁₀=1 if G is the base at position 10 on the sense strand, otherwise itsvalue is 0;G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;G₁₉=1 if G is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;U₁=1 if U is the base at position 1 on the sense strand, otherwise itsvalue is 0;U₂=1 if U is the base at position 2 on the sense strand, otherwise itsvalue is 0;U₃=1 if U is the base at position 3 on the sense strand, otherwise itsvalue is 0;U₄=1 if U is the base at position 4 on the sense strand, otherwise itsvalue is 0;U₇=1 if U is the base at position 7 on the sense strand, otherwise itsvalue is 0;U₉=1 if U is the base at position 9 on the sense strand, otherwise itsvalue is 0;U₁₀=1 if U is the base at position 10 on the sense strand, otherwise itsvalue is 0;U₁₅=1 if U is the base at position 15 on the sense strand, otherwise itsvalue is 0;U₁₆=1 if U is the base at position 16 on the sense strand, otherwise itsvalue is 0;U₁₇=1 if U is the base at position 17 on the sense strand, otherwise itsvalue is 0;U₁₈=1 if U is the base at position 18 on the sense strand, otherwise itsvalue is 0.GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sensestrand, or within positions 15-18 if the sense strand is only 18 basepairs in length;GC_(total)=the number of G and C bases in the sense strand;T_(m)=100 if the siRNA oligo has the internal repeat longer then 4 basepairs, otherwise its value is 0; andX=the number of times that the same nucleotide repeats four or moretimes in a row.

According to a fourth embodiment, the invention provides a method fordeveloping an algorithm for selecting siRNA, said method comprising: (a)selecting a set of siRNA; (b) measuring gene silencing ability of eachsiRNA from said set; (c) determining relative functionality of eachsiRNA; (d) determining improved functionality by the presence or absenceof at least one variable selected from the group consisting of thepresence or absence of a particular nucleotide at a particular position,the total number of As and Us in positions 15-19, the number of timesthat the same nucleotide repeats within a given sequence, and the totalnumber of Gs and Cs; and (e) developing an algorithm using theinformation of step (d).

According to a fifth embodiment, the present invention provides a kit,wherein said kit is comprised of at least two siRNAs, wherein said atleast two siRNAs comprise a first optimized siRNA and a second optimizedsiRNA, wherein said first optimized siRNA and said second optimizedsiRNA are optimized according a formula comprising Formula X.

The present invention also provides a method for identifying ahyperfunctional siRNA, comprising applying selection criteria to a setof potential siRNA that comprise 18-30 base pairs, wherein saidselection criteria are non-target specific criteria, and said setcomprises at least two siRNAs and each of said at least two siRNAscontains a sequence that is at least substantially complementary to atarget gene; determining the relative functionality of the at least twosiRNAs and assigning each of the at least two siRNAs a functionalityscore; and selecting siRNAs from the at least two siRNAs that have afunctionality score that reflects greater than 80 percent silencing at aconcentration in the picomolar range, wherein said greater than 80percent silencing endures for greater than 120 hours.

According to a sixth embodiment, the present invention provides ahyperfunctional siRNA that is capable of silencing Bc12.

According to a seventh embodiment, the present invention provides amethod for developing an siRNA algorithm for selecting functional andhyperfunctional siRNAs for a given sequence. The method comprises:

(a) selecting a set of siRNAs;

(b) measuring the gene silencing ability of each siRNA from said set;

(c) determining the relative functionality of each siRNA;

(d) determining the amount of improved functionality by the presence orabsence of at least one variable selected from the group consisting ofthe total GC content, melting temperature of the siRNA, GC content atpositions 15-19, the presence or absence of a particular nucleotide at aparticular position, relative thermodynamic stability at particularpositions in a duplex, and the number of times that the same nucleotiderepeats within a given sequence; and

(e) developing an algorithm using the information of step (d).

According to this embodiment, preferably the set of siRNAs comprises atleast 90 siRNAs from at least one gene, more preferably at least 180siRNAs from at least two different genes, and most preferably at least270 and 360 siRNAs from at least three and four different genes,respectively. Additionally, in step (d) the determination is made withpreferably at least two, more preferably at least three, even morepreferably at least four, and most preferably all of the variables. Theresulting algorithm is not target sequence specific.

In another embodiment, the present invention provides rationallydesigned siRNAs identified using the formulas above.

In yet another embodiment, the present invention is directed tohyperfunctional siRNA.

The ability to use the above algorithms, which are not sequence orspecies specific, allows for the cost-effective selection of optimizedsiRNAs for specific target sequences. Accordingly, there will be bothgreater efficiency and reliability in the use of siRNA technologies.

In various embodiments, siRNAs that target cystic fibrosis transmembraneconductance regulator, ATP-binding cassette (sub-family C, member 7)(CFTR, also known as CF, MRP7, ABC35 and ABCC7) are provided. In variousembodiments, the siRNAs are rationally designed. In various embodiments,the siRNAs are functional or hyperfunctional.

In various embodiments, an siRNA that targets CFTR is provided, whereinthe siRNA is selected from the group consisting of various siRNAsequences targeting CFTR that are disclosed herein. In variousembodiments, the siRNA sequence is selected from the group consisting ofSEQ ID NO. 438 to SEQ ID NO. 640.

In various embodiments, siRNA comprising a sense region and an antisenseregion are provided, said sense region and said antisense regiontogether form a duplex region comprising 18-30 base pairs, and saidsense region comprises a sequence that is at least 90% similar to asequence selected from the group consisting of siRNA sequences targetingCFTR that are disclosed herein. In various embodiments, the siRNAsequence is selected from the group consisting of SEQ ID NO. 438 to SEQID NO. 640.

In various embodiments, an siRNA comprising a sense region and anantisense region is provided, said sense region and said antisenseregion together form a duplex region comprising 18-30 base pairs, andsaid sense region comprises a sequence that is identical to a contiguousstretch of at least 18 bases of a sequence selected from the groupconsisting of SEQ ID NO. 438 to SEQ ID NO. 640. In various embodiments,the duplex region is 19-30 base pairs, and the sense region comprises asequence that is identical to a sequence selected from the groupconsisting of SEQ ID NO. 438 to SEQ ID NO. 640.

In various embodiments, a pool of at least two siRNAs is provided,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprising a duplex region of length 18-30 base pairs that has afirst sense region that is at least 90% similar to 18 bases of a firstsequence selected from the group consisting of SEQ ID NO. 438 to SEQ IDNO. 640, and said second siRNA comprises a duplex region of length 18-30base pairs that has a second sense region that is at least 90% similarto 18 bases of a second sequence selected from the group consisting ofSEQ ID NO. 438 to SEQ ID NO. 640, wherein said first sense region andsaid second sense region are not identical.

In various embodiments, the first sense region comprises a sequence thatis identical to at least 18 bases of a sequence selected from the groupconsisting of SEQ ID NO. 438 to SEQ ID NO. 640, and said second senseregion comprises a sequence that is identical to at least 18 bases of asequence selected from the group consisting of SEQ ID NO. 438 to SEQ IDNO. 640. In various embodiments, the duplex of said first siRNA is 19-30base pairs, and said first sense region comprises a sequence that is atleast 90% similar to a sequence selected from the group consisting ofSEQ ID NO. 438 to SEQ ID NO. 640, and said duplex of said second siRNAis 19-30 base pairs and comprises a sequence that is at least 90%similar to a sequence selected from the group consisting of SEQ ID NO.438 to SEQ ID NO. 640.

In various embodiments, the duplex of said first siRNA is 19-30 basepairs and said first sense region comprises a sequence that is identicalto at least 18 bases of a sequence selected from the group consisting ofSEQ ID NO. 438 to SEQ ID NO. 640, and said duplex of said second siRNAis 19-30 base pairs and said second region comprises a sequence that isidentical to a sequence selected from the group consisting of SEQ ID NO.438 to SEQ ID NO. 640.

For a better understanding of the present invention together with otherand further advantages and embodiments, reference is made to thefollowing description taken in conjunction with the examples, the scopeof which is set forth in the appended claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a model for siRNA-RISC interactions. RISC has the abilityto interact with either end of the siRNA or miRNA molecule. Followingbinding, the duplex is unwound, and the relevant target is identified,cleaved, and released.

FIG. 2 is a representation of the functionality of two hundred andseventy siRNA duplexes that were generated to target human cyclophilin,human diazepam-binding inhibitor (DB), and firefly luciferase.

FIG. 3 a is a representation of the silencing effect of 30 siRNAs inthree different cells lines, HEK293, DU145, and Hela. FIG. 3 b shows thefrequency of different functional groups (>95% silencing (black), >80%silencing (gray), >50% silencing (dark gray), and <50% silencing(white)) based on GC content. In cases where a given bar is absent froma particular GC percentage, no siRNA were identified for that particulargroup. FIG. 3 c shows the frequency of different functional groups basedon melting temperature (Tm).

FIG. 4 is a representation of a statistical analysis that revealedcorrelations between silencing and five sequence-related properties ofsiRNA: (A) an A at position 19 of the sense strand, (B) an A at position3 of the sense strand, (C) a U at position 10 of the sense strand, (D) abase other than G at position 13 of the sense strand, and (E) a baseother than C at position 19 of the sense strand. All variables werecorrelated with siRNA silencing of firefly luciferase and humancyclophilin. siRNAs satisfying the criterion are grouped on the left(Selected) while those that do not, are grouped on the right(Eliminated). Y-axis is “% Silencing of Control.” Each position on theX-axis represents a unique siRNA.

FIGS. 5A and 5B are representations of firefly luciferase andcyclophilin siRNA panels sorted according to functionality and predictedvalues using Formula VIII. The siRNA found within the circle representthose that have Formula VIII values (SMARTSCORES™, or siRNA rank) abovezero. siRNA outside the indicated area have calculated Formula VIIIvalues that are below zero. Y-axis is “Expression (% Control).” Eachposition on the X-axis represents a unique siRNA.

FIG. 6A is a representation of the average internal stability profile(AISP) derived from 270 siRNAs taken from three separate genes(cyclophilin B, DBI and firefly luciferase). Graphs represent AISPvalues of highly functional, functional, and non-functional siRNA. FIG.6B is a comparison between the AISP of naturally derived GFP siRNA(filled squares) and the AISP of siRNA from cyclophilin B, DBI, andluciferase having >90% silencing properties (no fill) for the antisensestrand. “DG” is the symbol for AG, free energy.

FIG. 7 is a histogram showing the differences in duplex functionalityupon introduction of base pair mismatches. The X-axis shows the mismatchintroduced in the siRNA and the position it is introduced (e.g., 8C>Areveals that position 8 (which normally has a C) has been changed to anA). The Y-axis is “% Silencing (Normalized to Control).” The samples onthe X-axis represent siRNAs at 100 nM and are, reading from left toright: 1A to C, 1A to G, 1A to U; 2A to C, 2A to G, 2A to U; 3A to C, 3Ato G, 3A to U; 4G to A, 4G to C; 4G to U; 5U to A, 5U to C, 5U to G; 6Uto A, 6U to C, 6U to G; 7G to A, 7G to C, 7G to U; 8C to A, 8C to G, 8Cto U; 9G to A, 9G to C, 9G to U; 10C to A, 10C to G, 10C to U; 11G to A,11G to C, 11G to U; 12G to A, 12G to C, 12G to U; 13A to C, 13A to G,13A to U; 14G to A, 14G to C, 14G to U; 15G to A, 15G to C, 15G to U;16A to C, 16A to G, 16A to U; 17G to A, 17G to C, 17G to U; 18U to A,18U to C, 18U to G; 19U to A, 19U to C, 19U to G; 20 wt; Control.

FIG. 8A is histogram that shows the effects of 5′ sense and antisensestrand modification with 2′-O-methylation on functionality. FIG. 8B isan expression profile showing a comparison of sense strand off-targeteffects for IGF1R-3 and 2′-O-methyl IGF1R-3. Sense strand off-targets(lower box) are not induced when the 5′ end of the sense strand ismodified with 2′-O-methyl groups (top box).

FIG. 9 shows a graph of SMARTSCORES™, or siRNA rank, versus RNAisilencing values for more than 360 siRNA directed against 30 differentgenes. SiRNA to the right of the vertical bar represent those siRNA thathave desirable SMARTSCORES™, or siRNA rank.

FIGS. 10A-E compare the RNAi of five different genes (SEAP, DBI, PLK,Firefly Luciferase, and Renilla Luciferase) by varying numbers ofrandomly selected siRNA and four rationally designed (SMART-selected)siRNA chosen using the algorithm described in Formula VIII. In addition,RNAi induced by a pool of the four SMART-selected siRNA is reported attwo different concentrations (100 and 400 nM). 10F is a comparisonbetween a pool of randomly selected EGFR siRNA (Pool 1) and a pool ofSMART-selected EGFR siRNA (Pool 2). Pool 1, S1-S4 and Pool 2 S1-S4represent the individual members that made up each respective pool. Notethat numbers for random siRNAs represent the position of the 5′ end ofthe sense strand of the duplex. The Y-axis represents the % expressionof the control(s). The X-axis is the percent expression of the control.

FIG. 11 shows the Western blot results from cells treated with siRNAdirected against twelve different genes involved in theclathrin-dependent endocytosis pathway (CHC, DynII, CALM, CLCa, CLCb,Eps15, Eps15R, Rab5a, Rab5b, Rab5c, β2 subunit of AP-2 and EEA.1). siRNAwere selected using Formula VIII. “Pool” represents a mixture ofduplexes 1-4. Total concentration of each siRNA in the pool is 25 nM.Total concentration=4×25=100 nM.

FIG. 12 is a representation of the gene silencing capabilities ofrationally-selected siRNA directed against ten different genes (humanand mouse cyclophilin, C-myc, human lamin A/C, QB (ubiquinol-cytochromec reductase core protein I), MEK1 and MEK2, ATE1 (arginyl-tRNA proteintransferase), GAPDH, and Eg5). The Y-axis is the percent expression ofthe control. Numbers 1, 2, 3 and 4 represent individual rationallyselected siRNA. “Pool” represents a mixture of the four individualsiRNA.

FIG. 13 is the sequence of the top ten Bcl2 siRNAs as determined byFormula VIII. Sequences are listed 5′ to 3′.

FIG. 14 is the knockdown by the top ten Bcl2 siRNAs at 100 nMconcentrations. The Y-axis represents the amount of expression relativeto the non-specific (ns) and transfection mixture control.

FIG. 15 represents a functional walk where siRNA beginning on everyother base pair of a region of the luciferase gene are tested for theability to silence the luciferase gene. The Y-axis represents thepercent expression relative to a control. The X-axis represents theposition of each individual siRNA. Reading from left to right across theX-axis, the position designations are 1, 3, 5, 7, 9, 11, 13, 15, 17, 19,21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55,57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, andPlasmid.

FIGS. 16A and 16B are histograms demonstrating the inhibition of targetgene expression by pools of 2 (16A) and 3 (16B) siRNA duplexes takenfrom the walk described in FIG. 15. The Y-axis in each represents thepercent expression relative to control. The X-axis in each representsthe position of the first siRNA in paired pools, or trios of siRNAs. Forinstance, the first paired pool contains siRNAs 1 and 3. The secondpaired pool contains siRNAs 3 and 5. Pool 3 (of paired pools) containssiRNAs 5 and 7, and so on. For each of 16A and 16B, the X-axis from leftto right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29,31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65,67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 17A and 17B are histograms demonstrating the inhibition of targetgene expression by pools of 4 (17A) and 5 (17B) siRNA duplexes. TheY-axis in each represents the percent expression relative to control.The X-axis in each represents the position of the first siRNA in eachpool. For each of 17A and 17B, the X-axis from left to right reads 1, 3,5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41,43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77,79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 18A and 18B are histograms demonstrating the inhibition of targetgene expression by siRNAs that are ten (18A) and twenty (18B) base pairsbase pairs apart. The Y-axis represents the percent expression relativeto a control. The X-axis represents the position of the first siRNA ineach pool. For each of 18A and 18B, the X-axis from left to right reads1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37,39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73,75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 19 shows that pools of siRNAs (dark gray bar) work as well (orbetter) than the best siRNA in the pool (light gray bar). The Y-axisrepresents the percent expression relative to a control. The X-axisrepresents the position of the first siRNA in each pool. The X-axis fromleft to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27,29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63,65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 20 shows that the combination of several semifunctional siRNAs(dark gray) result in a significant improvement of gene expressioninhibition over individual (semi-functional; light gray) siRNA. TheY-axis represents the percent expression relative to a control.

FIGS. 21A, 21B and 21C show both pools (Library, Lib) and individualsiRNAs in inhibition of gene expression of Beta-Galactosidase, RenillaLuciferase and SEAP (alkaline phosphatase). Numbers on the X-axisindicate the position of the 5′-most nucleotide of the sense strand ofthe duplex. The Y-axis represents the percent expression of each generelative to a control. Libraries contain 19 nucleotide long siRNAs (notincluding overhangs) that begin at the following nucleotides: SEAP: Lib1: 206, 766, 812, 923, Lib 2: 1117, 1280, 1300, 1487, Lib 3: 206, 766,812, 923, 1117, 1280, 1300, 1487, Lib 4: 206, 812, 1117, 1300, Lib 5:766, 923, 1280, 1487, Lib 6: 206, 1487; Bgal: Lib 1: 979, 1339, 2029,2590, Lib 2: 1087, 1783, 2399, 3257, Lib 3: 979, 1783, 2590, 3257, Lib4: 979, 1087, 1339, 1783, 2029, 2399, 2590, 3257, Lib 5: 979, 1087,1339, 1783, Lib 6: 2029, 2399, 2590, 3257; Renilla: Lib 1: 174, 300,432, 568, Lib 2: 592, 633, 729, 867, Lib 3: 174, 300, 432, 568, 592,633, 729, 867, Lib 4: 174, 432, 592, 729, Lib 5: 300, 568, 633, 867, Lib6: 592, 568.

FIG. 22 shows the results of an EGFR and TfnR internalization assay whensingle gene knockdowns are performed. The Y-axis represents percentinternalization relative to control.

FIG. 23 shows the results of an EGFR and TfnR internalization assay whenmultiple genes are knocked down (e.g., Rab5a, b, c). The Y-axisrepresents the percent internalization relative to control.

FIG. 24 shows the simultaneous knockdown of four different genes. siRNAsdirected against G6PD, GAPDH, PLK, and UQC were simultaneouslyintroduced into cells. Twenty-four hours later, cultures were harvestedand assayed for mRNA target levels for each of the four genes. Acomparison is made between cells transfected with individual siRNAs vs.a pool of siRNAs directed against all four genes.

FIG. 25 shows the functionality of ten siRNAs at 0.3 nM concentrations.

DETAILED DESCRIPTION Definitions

Unless stated otherwise, the following terms and phrases have themeanings provided below:

Complementary

The term “complementary” refers to the ability of polynucleotides toform base pairs with one another. Base pairs are typically formed byhydrogen bonds between nucleotide units in antiparallel polynucleotidestrands. Complementary polynucleotide strands can base pair in theWatson-Crick manner (e.g., A to T, A to U, C to G), or in any othermanner that allows for the formation of duplexes. As persons skilled inthe art are aware, when using RNA as opposed to DNA, uracil rather thanthymine is the base that is considered to be complementary to adenosine.However, when a U is denoted in the context of the present invention,the ability to substitute a T is implied, unless otherwise stated.

Perfect complementarity or 100% complementarity refers to the situationin which each nucleotide unit of one polynucleotide strand can hydrogenbond with a nucleotide unit of a second polynucleotide strand. Less thanperfect complementarity refers to the situation in which some, but notall, nucleotide units of two strands can hydrogen bond with each other.For example, for two 20-mers, if only two base pairs on each strand canhydrogen bond with each other, the polynucleotide strands exhibit 10%complementarity. In the same example, if 18 base pairs on each strandcan hydrogen bond with each other, the polynucleotide strands exhibit90% complementarity.

Deoxynucleotide

The term “deoxynucleotide” refers to a nucleotide or polynucleotidelacking a hydroxyl group (OH group) at the 2′ and/or 3′ position of asugar moiety. Instead, it has a hydrogen bonded to the 2′ and/or 3′carbon. Within an RNA molecule that comprises one or moredeoxynucleotides, “deoxynucleotide” refers to the lack of an OH group atthe 2′ position of the sugar moiety, having instead a hydrogen bondeddirectly to the 2′ carbon.

Deoxyribonucleotide

The terms “deoxyribonucleotide” and “DNA” refer to a nucleotide orpolynucleotide comprising at least one sugar moiety that has an H,rather than an OH, at its 2′ and/or 3′position.

Duplex Region

The phrase “duplex region” refers to the region in two complementary orsubstantially complementary polynucleotides that form base pairs withone another, either by Watson-Crick base pairing or any other mannerthat allows for a stabilized duplex between polynucleotide strands thatare complementary or substantially complementary. For example, apolynucleotide strand having 21 nucleotide units can base pair withanother polynucleotide of 21 nucleotide units, yet only 19 bases on eachstrand are complementary or substantially complementary, such that the“duplex region” has 19 base pairs. The remaining bases may, for example,exist as 5′ and 3′ overhangs. Further, within the duplex region, 100%complementarity is not required; substantial complementarity isallowable within a duplex region. Substantial complementarity refers to79% or greater complementarity. For example, a mismatch in a duplexregion consisting of 19 base pairs results in 94.7% complementarity,rendering the duplex region substantially complementary.

Filters

The term “filter” refers to one or more procedures that are performed onsequences that are identified by the algorithm. In some instances,filtering includes in silico procedures where sequences identified bythe algorithm can be screened to identify duplexes carrying desirable orundesirable motifs. Sequences carrying such motifs can be selected for,or selected against, to obtain a final set with the preferredproperties. In other instances, filtering includes wet lab experiments.For instance, sequences identified by one or more versions of thealgorithm can be screened using any one of a number of procedures toidentify duplexes that have hyperfunctional traits (e.g., they exhibit ahigh degree of silencing at subnanomolar concentrations and/or exhibithigh degrees of silencing longevity).

Gene Silencing

The phrase “gene silencing” refers to a process by which the expressionof a specific gene product is lessened or attenuated. Gene silencing cantake place by a variety of pathways. Unless specified otherwise, as usedherein, gene silencing refers to decreases in gene product expressionthat results from RNA interference (RNAi), a defined, though partiallycharacterized pathway whereby small inhibitory RNA (siRNA) act inconcert with host proteins (e.g., the RNA induced silencing complex,RISC) to degrade messenger RNA (mRNA) in a sequence-dependent fashion.The level of gene silencing can be measured by a variety of means,including, but not limited to, measurement of transcript levels byNorthern Blot Analysis, B-DNA techniques, transcription-sensitivereporter constructs, expression profiling (e.g., DNA chips), and relatedtechnologies. Alternatively, the level of silencing can be measured byassessing the level of the protein encoded by a specific gene. This canbe accomplished by performing a number of studies including WesternAnalysis, measuring the levels of expression of a reporter protein thathas e.g., fluorescent properties (e.g., GFP) or enzymatic activity(e.g., alkaline phosphatases), or several other procedures.

miRNA

The term “miRNA” refers to microRNA.

Nucleotide

The term “nucleotide” refers to a ribonucleotide or adeoxyribonucleotide or modified form thereof, as well as an analogthereof. Nucleotides include species that comprise purines, e.g.,adenine, hypoxanthine, guanine, and their derivatives and analogs, aswell as pyrimidines, e.g., cytosine, uracil, thymine, and theirderivatives and analogs.

Nucleotide analogs include nucleotides having modifications in thechemical structure of the base, sugar and/or phosphate, including, butnot limited to, 5-position pyrimidine modifications, 8-position purinemodifications, modifications at cytosine exocyclic amines, andsubstitution of 5-bromo-uracil; and 2′-position sugar modifications,including but not limited to, sugar-modified ribonucleotides in whichthe 2′-OH is replaced by a group such as an H, OR, R, halo, SH, SR, NH₂,NHR, NR₂, or CN, wherein R is an alkyl moiety. Nucleotide analogs arealso meant to include nucleotides with bases such as inosine, queuosine,xanthine, sugars such as 2′-methyl ribose, non-natural phosphodiesterlinkages such as methylphosphonates, phosphorothioates and peptides.

Modified bases refer to nucleotide bases such as, for example, adenine,guanine, cytosine, thymine, uracil, xanthine, inosine, and queuosinethat have been modified by the replacement or addition of one or moreatoms or groups. Some examples of types of modifications that cancomprise nucleotides that are modified with respect to the base moietiesinclude but are not limited to, alkylated, halogenated, thiolated,aminated, amidated, or acetylated bases, individually or in combination.More specific examples include, for example, 5-propynyluridine,5-propynylcytidine, 6-methyladenine, 6-methylguanine,N,N,-dimethyladenine, 2-propyladenine, 2-propylguanine, 2-aminoadenine,1-methylinosine, 3-methyluridine, 5-methylcytidine, 5-methyluridine andother nucleotides having a modification at the 5 position,5-(2-amino)propyl uridine, 5-halocytidine, 5-halouridine,4-acetylcytidine, 1-methyladenosine, 2-methyladenosine,3-methylcytidine, 6-methyluridine, 2-methylguanosine, 7-methylguanosine,2,2-dimethylguanosine, 5-methylaminoethyluridine, 5-methyloxyuridine,deazanucleotides such as 7-deaza-adenosine, 6-azouridine, 6-azocytidine,6-azothymidine, 5-methyl-2-thiouridine, other thio bases such as2-thiouridine and 4-thiouridine and 2-thiocytidine, dihydrouridine,pseudouridine, queuosine, archaeosine, naphthyl and substituted naphthylgroups, any O- and N-alkylated purines and pyrimidines such asN6-methyladenosine, 5-methylcarbonylmethyluridine, uridine 5-oxyaceticacid, pyridine-4-one, pyridine-2-one, phenyl and modified phenyl groupssuch as aminophenol or 2,4,6-trimethoxy benzene, modified cytosines thatact as G-clamp nucleotides, 8-substituted adenines and guanines,5-substituted uracils and thymines, azapyrimidines, carboxyhydroxyalkylnucleotides, carboxyalkylaminoalkyl nucleotides, andalkylcarbonylalkylated nucleotides. Modified nucleotides also includethose nucleotides that are modified with respect to the sugar moiety, aswell as nucleotides having sugars or analogs thereof that are notribosyl. For example, the sugar moieties may be, or be based on,mannoses, arabinoses, glucopyranoses, galactopyranoses, 4′-thioribose,and other sugars, heterocycles, or carbocycles.

The term nucleotide is also meant to include what are known in the artas universal bases. By way of example, universal bases include but arenot limited to 3-nitropyrrole, 5-nitroindole, or nebularine. The term“nucleotide” is also meant to include the N3‘to PS’ phosphoramidate,resulting from the substitution of a ribosyl 3′ oxygen with an aminegroup.

Further, the term nucleotide also includes those species that have adetectable label, such as for example a radioactive or fluorescentmoiety, or mass label attached to the nucleotide.

Off-Target Silencing and Off-Target Interference

The phrases “off-target silencing” and “off-target interference” aredefined as degradation of mRNA other than the intended target mRNA dueto overlapping and/or partial homology with secondary mRNA messages.

Polynucleotide

The term “polynucleotide” refers to polymers of nucleotides, andincludes but is not limited to DNA, RNA, DNA/RNA hybrids includingpolynucleotide chains of regularly and/or irregularly alternatingdeoxyribosyl moieties and ribosyl moieties (i.e., wherein alternatenucleotide units have an —OH, then and —H, then an —OH, then an —H, andso on at the 2′ position of a sugar moiety), and modifications of thesekinds of polynucleotides, wherein the attachment of various entities ormoieties to the nucleotide units at any position are included.

Polyribonucleotide

The term “polyribonucleotide” refers to a polynucleotide comprising twoor more modified or unmodified ribonucleotides and/or their analogs. Theterm “polyribonucleotide” is used interchangeably with the term“oligoribonucleotide.”

Ribonucleotide and Ribonucleic Acid

The term “ribonucleotide” and the phrase “ribonucleic acid” (RNA), referto a modified or unmodified nucleotide or polynucleotide comprising atleast one ribonucleotide unit. A ribonucleotide unit comprises anhydroxyl group attached to the 2′ position of a ribosyl moiety that hasa nitrogenous base attached in N-glycosidic linkage at the 1′ positionof a ribosyl moiety, and a moiety that either allows for linkage toanother nucleotide or precludes linkage.

siRNA

The term “siRNA” refers to small inhibitory RNA duplexes that induce theRNA interference (RNAi) pathway. These molecules can vary in length(generally 18-30 base pairs) and contain varying degrees ofcomplementarity to their target mRNA in the antisense strand. Some, butnot all, siRNA have unpaired overhanging bases on the 5′ or 3′ end ofthe sense strand and/or the antisense strand. The term “siRNA” includesduplexes of two separate strands, as well as single strands that canform hairpin structures comprising a duplex region.

siRNA may be divided into five (5) groups (non-functional,semi-functional, functional, highly functional, and hyper-functional)based on the level or degree of silencing that they induce in culturedcell lines. As used herein, these definitions are based on a set ofconditions where the siRNA is transfected into said cell line at aconcentration of 100 nM and the level of silencing is tested at a timeof roughly 24 hours after transfection, and not exceeding 72 hours aftertransfection. In this context, “non-functional siRNA” are defined asthose siRNA that induce less than 50% (<50%) target silencing.“Semi-functional siRNA” induce 50-79% target silencing. “FunctionalsiRNA” are molecules that induce 80-95% gene silencing.“Highly-functional siRNA” are molecules that induce greater than 95%gene silencing. “Hyperfunctional siRNA” are a special class ofmolecules. For purposes of this document, hyperfunctional siRNA aredefined as those molecules that: (1) induce greater than 95% silencingof a specific target when they are transfected at subnanomolarconcentrations (i.e., less than one nanomolar); and/or (2) inducefunctional (or better) levels of silencing for greater than 96 hours.These relative functionalities (though not intended to be absolutes) maybe used to compare siRNAs to a particular target for applications suchas functional genomics, target identification and therapeutics.

SMARTSCORE™, or siRNA Rank

The term “SMARTSCORE™”, or “siRNA rank” refers to a number determined byapplying any of the formulas to a given siRNA sequence. The term“SMART-selected” or “rationally selected” or “rational selection” refersto siRNA that have been selected on the basis of their SMARTSCORES™, orsiRNA ranking.

Substantially Similar

The phrase “substantially similar” refers to a similarity of at least90% with respect to the identity of the bases of the sequence.

Target

The term “target” is used in a variety of different forms throughoutthis document and is defined by the context in which it is used. “TargetmRNA” refers to a messenger RNA to which a given siRNA can be directedagainst. “Target sequence” and “target site” refer to a sequence withinthe mRNA to which the sense strand of an siRNA shows varying degrees ofhomology and the antisense strand exhibits varying degrees ofcomplementarity. The phrase “siRNA target” can refer to the gene, mRNA,or protein against which an siRNA is directed. Similarly, “targetsilencing” can refer to the state of a gene, or the corresponding mRNAor protein.

Transfection

The term “transfection” refers to a process by which agents areintroduced into a cell. The list of agents that can be transfected islarge and includes, but is not limited to, siRNA, sense and/oranti-sense sequences, DNA encoding one or more genes and organized intoan expression plasmid, proteins, protein fragments, and more. There aremultiple methods for transfecting agents into a cell including, but notlimited to, electroporation, calcium phosphate-based transfections,DEAE-dextran-based transfections, lipid-based transfections, molecularconjugate-based transfections (e.g., polylysine-DNA conjugates),microinjection and others.

The present invention is directed to improving the efficiency of genesilencing by siRNA. Through the inclusion of multiple siRNA sequencesthat are targeted to a particular gene and/or selecting an siRNAsequence based on certain defined criteria, improved efficiency may beachieved.

The present invention will now be described in connection with preferredembodiments. These embodiments are presented in order to aid in anunderstanding of the present invention and are not intended, and shouldnot be construed, to limit the invention in any way. All alternatives,modifications and equivalents that may become apparent to those ofordinary skill upon reading this disclosure are included within thespirit and scope of the present invention.

Furthermore, this disclosure is not a primer on RNA interference. Basicconcepts known to persons skilled in the art have not been set forth indetail.

The present invention is directed to increasing the efficiency of RNAi,particularly in mammalian systems. Accordingly, the present inventionprovides kits, siRNAs and methods for increasing siRNA efficacy.

According to a first embodiment, the present invention provides a kitfor gene silencing, wherein said kit is comprised of a pool of at leasttwo siRNA duplexes, each of which is comprised of a sequence that iscomplementary to a portion of the sequence of one or more targetmessenger RNA, and each of which is selected using non-target specificcriteria. Each of the at least two siRNA duplexes of the kitcomplementary to a portion of the sequence of one or more target mRNAsis preferably selected using Formula X.

According to a second embodiment, the present invention provides amethod for selecting an siRNA, said method comprising applying selectioncriteria to a set of potential siRNA that comprise 18-30 base pairs,wherein said selection criteria are non-target specific criteria, andsaid set comprises at least two siRNAs and each of said at least twosiRNAs contains a sequence that is at least substantially complementaryto a target gene; and determining the relative functionality of the atleast two siRNAs.

In one embodiment, the present invention also provides a method whereinsaid selection criteria are embodied in a formula comprising:

$\begin{matrix}{{{\left( {- 14} \right)*G_{13}} - {13*A_{1}} - {12*U_{7}} - {11*U_{2}} - {10*A_{11}} - {10*U_{4}} - {10*C_{3}} - {10*C_{5}} - {10*C_{6}} - {9*A_{10}} - {9*U_{9}} - {9*C_{18}} - {8*G_{10}} - {7*U_{1}} - {7*U_{16}} - {7*C_{17}} - {7*C_{19}} + {7*U_{17}} + {8*A_{2}} + {8*A_{4}} + {8*A_{5}} + {8*C_{4}} + {9*G_{8}} + {10*A_{7}} + {10*U_{18}} + {11*A_{19}} + {11*C_{9}} + {15*G_{1}} + {18*A_{3}} + {19*U_{10}} - {Tm} - {3*\left( {GC}_{total} \right)} - {6*\left( {GC}_{15\text{-}19} \right)} - {30*X}};{or}} & {{Formula}\mspace{14mu} {VIII}} \\{{{\left( {- 8} \right)*A\; 1} + {\left( {- 1} \right)*A\; 2} + {(12)*A\; 3} + {(7)*A\; 4} + {(18)*A\; 5} + {(12)*A\; 6} + {(19)*A\; 7} + {(6)*A\; 8} + {\left( {- 4} \right)*A\; 9} + {\left( {- 5} \right)*A\; 10} + {\left( {- 2} \right)*A\; 11} + {\left( {- 5} \right)*A\; 12} + {(17)*A\; 13} + {\left( {- 3} \right)*A\; 14} + {(4)*A\; 15} + {(2)*A\; 16} + {(8)*A\; 17} + {(11)*A\; 18} + {(30)*A\; 19} + {\left( {- 13} \right)*U\; 1} + {\left( {- 10} \right)*U\; 2} + {(2)*U\; 3} + {\left( {- 2} \right)*U\; 4} + {\left( {- 5} \right)*U\; 5} + {(5)*U\; 6} + {\left( {- 2} \right)*U\; 7} + {\left( {- 10} \right)*U\; 8} + {\left( {- 5} \right)*U\; 9} + {(15)*U\; 10} + {\left( {- 1} \right)*U\; 11} + {(0)*U\; 12} + {(10)*U\; 13} + {\left( {- 9} \right)*U\; 14} + {\left( {- 13} \right)*U\; 15} + {\left( {- 10} \right)*U\; 16} + {(3)*U\; 17} + {(9)*U\; 18} + {(9)*U\; 19} + {(7)*C\; 1} + {(3)*C\; 2} + {\left( {- 21} \right)*C\; 3} + {(5)*C\; 4} + {\left( {- 9} \right)*C\; 5} + {\left( {- 20} \right)*C\; 6} + {\left( {- 18} \right)*C\; 7} + {\left( {- 5} \right)*C\; 8} + {(5)*C\; 9} + {(1)*C\; 10} + {(2)*C\; 11} + {\left( {- 5} \right)*C\; 12} + {\left( {- 3} \right)*C\; 13} + {\left( {- 6} \right)*C\; 14} + {\left( {- 2} \right)*C\; 15} + {\left( {- 5} \right)*C\; 16} + {\left( {- 3} \right)*C\; 17} + {\left( {- 12} \right)*C\; 18} + {\left( {- 18} \right)*C\; 19} + {(14)*G\; 1} + {(8)*G\; 2} + {(7)*G\; 3} + {\left( {- 10} \right)*G\; 4} + {\left( {- 4} \right)*G\; 5} + {(2)*G\; 6} + {(1)*G\; 7} + {(9)*G\; 8} + {(5)*G\; 9} + {\left( {- 11} \right)*G\; 10} + {(1)*G\; 11} + {(9)*G\; 12} + {\left( {- 24} \right)*G\; 13} + {(18)*G\; 14} + {(11)*G\; 15} + {(13)*G\; 16} + {\left( {- 7} \right)*G\; 17} + {\left( {- 9} \right)*G\; 18} + {\left( {- 22} \right)*G\; 19} + {6*\left( {{{number}\mspace{14mu} {of}\mspace{14mu} A} + {U\mspace{14mu} {in}\mspace{14mu} {position}\mspace{14mu} 15\text{-}19}} \right)} - {3*\left( {{{number}\mspace{14mu} {of}{\mspace{11mu} \;}G} + {C\mspace{14mu} {in}\mspace{14mu} {whole}\mspace{14mu} {siRNA}}} \right)}},} & {{Formula}\mspace{14mu} X}\end{matrix}$

wherein position numbering begins at the 5′-most position of a sensestrand, and

A₁ if A is the base at position 1 of the sense strand, otherwise itsvalue is 0;

A₂=1 if A is the base at position 2 of the sense strand, otherwise itsvalue is 0;A₃=1 if A is the base at position 3 of the sense strand, otherwise itsvalue is 0;A₄=1 if A is the base at position 4 of the sense strand, otherwise itsvalue is 0;A₅=1 if A is the base at position 5 of the sense strand, otherwise itsvalue is 0;A₆=1 if A is the base at position 6 of the sense strand, otherwise itsvalue is 0;A₇=1 if A is the base at position 7 of the sense strand, otherwise itsvalue is 0;A₁₀=1 if A is the base at position 10 of the sense strand, otherwise itsvalue is 0;A₁₁=1 if A is the base at position 11 of the sense strand, otherwise itsvalue is 0;A₁₃=1 if A is the base at position 13 of the sense strand, otherwise itsvalue is 0;A₁₉=1 if A is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;

C₃=1 if C is the base at position 3 of the sense strand, otherwise itsvalue is 0;

C₄=1 if C is the base at position 4 of the sense strand, otherwise itsvalue is 0;C₅=1 if C is the base at position 5 of the sense strand, otherwise itsvalue is 0;C₆=1 if C is the base at position 6 of the sense strand, otherwise itsvalue is 0;C₇=1 if C is the base at position 7 of the sense strand, otherwise itsvalue is 0;C₉=1 if C is the base at position 9 of the sense strand, otherwise itsvalue is 0;C₁₇=1 if C is the base at position 17 of the sense strand, otherwise itsvalue is 0;C₁₈=1 if C is the base at position 18 of the sense strand, otherwise itsvalue is 0;C₁₉=1 if C is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;G₁=1 if G is the base at position 1 on the sense strand, otherwise itsvalue is 0;G₂=1 if G is the base at position 2 of the sense strand, otherwise itsvalue is 0;G₈=1 if G is the base at position 8 on the sense strand, otherwise itsvalue is 0;G₁₀=1 if G is the base at position 10 on the sense strand, otherwise itsvalue is 0;G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;G₁₉=1 if G is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;

U₁=1 if U is the base at position I on the sense strand, otherwise itsvalue is 0;

U₂=1 if U is the base at position 2 on the sense strand, otherwise itsvalue is 0;U₃=1 if U is the base at position 3 on the sense strand, otherwise itsvalue is 0;U₄=1 if U is the base at position 4 on the sense strand, otherwise itsvalue is 0;U₇=1 if U is the base at position 7 on the sense strand, otherwise itsvalue is 0;U₉=1 if U is the base at position 9 on the sense strand, otherwise itsvalue is 0; U₁₀=1 if U is the base at position 10 on the sense strand,otherwise its value is 0;U₁₅=1 if U is the base at position 15 on the sense strand, otherwise itsvalue is 0;U₁₆=1 if U is the base at position 16 on the sense strand, otherwise itsvalue is 0;U₁₇=1 if U is the base at position 17 on the sense strand, otherwise itsvalue is 0;U₁₈=1 if U is the base at position 18 on the sense strand, otherwise itsvalue is 0.

GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sensestrand, or within positions 15-18 if the sense strand is only 18 basepairs in length;

GC_(total)=the number of G and C bases in the sense strand;

Tm=100 if the siRNA oligo has the internal repeat longer then 4 basepairs, otherwise its value is 0; and

X=the number of times that the same nucleotide repeats four or moretimes in a row.

Any of the methods of selecting siRNA in accordance with the inventioncan further comprise comparing the internal stability profiles of thesiRNAs to be selected, and selecting those siRNAs with the mostfavorable internal stability profiles. Any of the methods of selectingsiRNA can further comprise selecting either for or against sequencesthat contain motifs that induce cellular stress. Such motifs include,for example, toxicity motifs. Any of the methods of selecting siRNA canfurther comprise either selecting for or selecting against sequencesthat comprise stability motifs.

In another embodiment, the present invention provides a method of genesilencing, comprising introducing into a cell at least one siRNAselected according to any of the methods of the present invention. ThesiRNA can be introduced by allowing passive uptake of siRNA, or throughthe use of a vector.

According to a third embodiment, the invention provides a method fordeveloping an algorithm for selecting siRNA, said method comprising: (a)selecting a set of siRNA; (b) measuring gene silencing ability of eachsiRNA from said set; (c) determining relative functionality of eachsiRNA; (d) determining improved functionality by the presence or absenceof at least one variable selected from the group consisting of thepresence or absence of a particular nucleotide at a particular position,the total number of As and Us in positions 15-19, the number of timesthat the same nucleotide repeats within a given sequence, and the totalnumber of Gs and Cs; and (e) developing an algorithm using theinformation of step (d).

In another embodiment, the invention provides a method for selecting ansiRNA with improved functionality, comprising using the above-mentionedalgorithm to identify an siRNA of improved functionality.

According to a fourth embodiment, the present invention provides a kit,wherein said kit is comprised of at least two siRNAs, wherein said atleast two siRNAs comprise a first optimized siRNA and a second optimizedsiRNA, wherein said first optimized siRNA and said second optimizedsiRNA are optimized according a formula comprising Formula X.

According to a fifth embodiment, the present invention provides a methodfor identifying a hyperfunctional siRNA, comprising applying selectioncriteria to a set of potential siRNA that comprise 18-30 base pairs,wherein said selection criteria are non-target specific criteria, andsaid set comprises at least two siRNAs and each of said at least twosiRNAs contains a sequence that is at least substantially complementaryto a target gene; determining the relative functionality of the at leasttwo siRNAs and assigning each of the at least two siRNAs a functionalityscore; and selecting siRNAs from the at least two siRNAs that have afunctionality score that reflects greater than 80 percent silencing at aconcentration in the picomolar range, wherein said greater than 80percent silencing endures for greater than 120 hours.

In other embodiments, the invention provides kits and/or methods whereinthe siRNA are comprised of two separate polynucleotide strands; whereinthe siRNA are comprised of a single contiguous molecule such as, forexample, a unimolecular siRNA (comprising, for example, either anucleotide or non-nucleotide loop); wherein the siRNA are expressed fromone or more vectors; and wherein two or more genes are silenced by asingle administration of siRNA.

According to a sixth embodiment, the present invention provides ahyperfunctional siRNA that is capable of silencing Bcl2.

According to a seventh embodiment, the present invention provides amethod for developing an siRNA algorithm for selecting functional andhyperfunctional siRNAs for a given sequence. The method comprises:

(a) selecting a set of siRNAs;

(b) measuring the gene silencing ability of each siRNA from said set;

(c) determining the relative functionality of each siRNA;

(d) determining the amount of improved functionality by the presence orabsence of at least one variable selected from the group consisting ofthe total GC content, melting temperature of the siRNA, GC content atpositions 15-19, the presence or absence of a particular nucleotide at aparticular position, relative thermodynamic stability at particularpositions in a duplex, and the number of times that the same nucleotiderepeats within a given sequence; and

(e) developing an algorithm using the information of step (d).

According to this embodiment, preferably the set of siRNAs comprises atleast 90 siRNAs from at least one gene, more preferably at least 180siRNAs from at least two different genes, and most preferably at least270 and 360 siRNAs from at least three and four different genes,respectively. Additionally, in step (d) the determination is made withpreferably at least two, more preferably at least three, even morepreferably at least four, and most preferably all of the variables. Theresulting algorithm is not target sequence specific.

In another embodiment, the present invention provides rationallydesigned siRNAs identified using the formulas above.

In yet another embodiment, the present invention is directed tohyperfunctional siRNA.

The ability to use the above algorithms, which are not sequence orspecies specific, allows for the cost-effective selection of optimizedsiRNAs for specific target sequences. Accordingly, there will be bothgreater efficiency and reliability in the use of siRNA technologies.

The methods disclosed herein can be used in conjunction with comparinginternal stability profiles of selected siRNAs, and designing an siRNAwith a desirable internal stability profile; and/or in conjunction witha selection either for or against sequences that contain motifs thatinduce cellular stress, for example, cellular toxicity.

Any of the methods disclosed herein can be used to silence one or moregenes by introducing an siRNA selected, or designed, in accordance withany of the methods disclosed herein. The siRNA(s) can be introduced intothe cell by any method known in the art, including passive uptake orthrough the use of one or more vectors.

Any of the methods and kits disclosed herein can employ eitherunimolecular siRNAs, siRNAs comprised of two separate polynucleotidestrands, or combinations thereof.

Any of the methods disclosed herein can be used in gene silencing, wheretwo or more genes are silenced by a single administration of siRNA(s).The siRNA(s) can be directed against two or more target genes, andadministered in a single dose or single transfection, as the case maybe.

Optimizing siRNA

According to one embodiment, the present invention provides a method forimproving the effectiveness of gene silencing for use to silence aparticular gene through the selection of an optimal siRNA. An siRNAselected according to this method may be used individually, or inconjunction with the first embodiment, i.e., with one or more othersiRNAs, each of which may or may not be selected by this criteria inorder to maximize their efficiency.

The degree to which it is possible to select an siRNA for a given mRNAthat maximizes these criteria will depend on the sequence of the mRNAitself. However, the selection criteria will be independent of thetarget sequence. According to this method, an siRNA is selected for agiven gene by using a rational design. That said, rational design can bedescribed in a variety of ways. Rational design is, in simplest terms,the application of a proven set of criteria that enhance the probabilityof identifying a functional or hyperfunctional siRNA. In one method,rationally designed siRNA can be identified by maximizing one or more ofthe following criteria:

(1) A low GC content, preferably between about 30-52%.

(2) At least 2, preferably at least 3 A or U bases at positions 15-19 ofthe siRNA on the sense strand.

(3) An A base at position 19 of the sense strand.(4) An A base at position 3 of the sense strand.(5) A U base at position 10 of the sense strand.(6) An A base at position 14 of the sense strand.(7) A base other than C at position 19 of the sense strand.(8) A base other than G at position 13 of the sense strand.

(9) A Tm, which refers to the character of the internal repeat thatresults in inter- or intramolecular structures for one strand of theduplex, that is preferably not stable at greater than 50° C., morepreferably not stable at greater than 37° C., even more preferably notstable at greater than 30° C. and most preferably not stable at greaterthan 20° C.

(10) A base other than U at position 5 of the sense strand.(11) A base other than A at position 11 of the sense strand.(12) A base other than an A at position 1 of the sense strand.(13) A base other than an A at position 2 of the sense strand.(14) An A base at position 4 of the sense strand.(15) An A base at position 5 of the sense strand.(16) An A base at position 6 of the sense strand.(17) An A base at position 7 of the sense strand.(18) An A base at position 8 of the sense strand.(19) A base other than an A at position 9 of the sense strand.(20) A base other than an A at position 10 of the sense strand.(21) A base other than an A at position 11 of the sense strand.(22) A base other than an A at position 12 of the sense strand.(23) An A base at position 13 of the sense strand.(24) A base other than an A at position 14 of the sense strand.(25) An A base at position 15 of the sense strand(26) An A base at position 16 of the sense strand.(27) An A base at position 17 of the sense strand.(28) An A base at position 18 of the sense strand.(29) A base other than a U at position 1 of the sense strand.(30) A base other than a U at position 2 of the sense strand.(31) A U base at position 3 of the sense strand.(32) A base other than a U at position 4 of the sense strand.(33) A base other than a U at position 5 of the sense strand.(34) A U base at position 6 of the sense strand.(35) A base other than a U at position 7 of the sense strand.(36) A base other than a U at position 8 of the sense strand.(37) A base other than a U at position 9 of the sense strand.(38) A base other than a U at position 11 of the sense strand.(39) A U base at position 13 of the sense strand.(40) A base other than a U at position 14 of the sense strand.(41) A base other than a U at position 15 of the sense strand.(42) A base other than a U at position 16 of the sense strand.(43) A U base at position 17 of the sense strand.(44) A U base at position 18 of the sense strand.(45) A U base at position 19 of the sense strand.(46) A C base at position 1 of the sense strand.(47) A C base at position 2 of the sense strand.(48) A base other than a C at position 3 of the sense strand.(49) A C base at position 4 of the sense strand.(50) A base other than a C at position 5 of the sense strand.(51) A base other than a C at position 6 of the sense strand.(52) A base other than a C at position 7 of the sense strand.(53) A base other than a C at position 8 of the sense strand.(54) A C base at position 9 of the sense strand.(55) A C base at position 10 of the sense strand.(56) A C base at position 11 of the sense strand.(57) A base other than a C at position 12 of the sense strand.(58) A base other than a C at position 13 of the sense strand.(59) A base other than a C at position 14 of the sense strand.(60) A base other than a C at position 15 of the sense strand.(61) A base other than a C at position 16 of the sense strand.(62) A base other than a C at position 17 of the sense strand.(63) A base other than a C at position 18 of the sense strand.(64) A G base at position 1 of the sense strand.(65) A G base at position 2 of the sense strand.(66) A G base at position 3 of the sense strand.(67) A base other than a G at position 4 of the sense strand.(68) A base other than a G at position 5 of the sense strand.(69) A G base at position 6 of the sense strand.(70) A G base at position 7 of the sense strand.(71) A G base at position 8 of the sense strand.(72) A G base at position 9 of the sense strand.(73) A base other than a G at position 10 of the sense strand.(74) A G base at position 11 of the sense strand.(75) A G base at position 12 of the sense strand.(76) A G base at position 14 of the sense strand.(77) A G base at position 15 of the sense strand.(78) A G base at position 16 of the sense strand.(79) A base other than a G at position 17 of the sense strand.(80) A base other than a G at position 18 of the sense strand.(81) A base other than a G at position 19 of the sense strand.

The importance of various criteria can vary greatly. For instance, a Cbase at position 10 of the sense strand makes a minor contribution toduplex functionality. In contrast, the absence of a C at position 3 ofthe sense strand is very important. Accordingly, preferably an siRNAwill satisfy as many of the aforementioned criteria as possible.

With respect to the criteria, GC content, as well as a high number of AUin positions 15-19 of the sense strand, may be important for easement ofthe unwinding of double stranded siRNA duplex. Duplex unwinding has beenshown to be crucial for siRNA functionality in vivo.

With respect to criterion 9, the internal structure is measured in termsof the melting temperature of the single strand of siRNA, which is thetemperature at which 50% of the molecules will become denatured. Withrespect to criteria 2-8 and 10-11, the positions refer to sequencepositions on the sense strand, which is the strand that is identical tothe mRNA.

In one preferred embodiment, at least criteria 1 and 8 are satisfied. Inanother preferred embodiment, at least criteria 7 and 8 are satisfied.In still another preferred embodiment, at least criteria 1, 8 and 9 aresatisfied.

It should be noted that all of the aforementioned criteria regardingsequence position specifics are with respect to the 5′ end of the sensestrand. Reference is made to the sense strand, because most databasescontain information that describes the information of the mRNA. Becauseaccording to the present invention a chain can be from 18 to 30 bases inlength, and the aforementioned criteria assumes a chain 19 base pairs inlength, it is important to keep the aforementioned criteria applicableto the correct bases.

When there are only 18 bases, the base pair that is not present is thebase pair that is located at the 3′ of the sense strand. When there aretwenty to thirty bases present, then additional bases are added at the5′ end of the sense chain and occupy positions ⁻1 to ⁻11. Accordingly,with respect to SEQ. ID NO. 0001 NNANANNNNUCNAANNNNA and SEQ.

ID NO. 0028 GUCNNANANNNNUCNAANNNNA, both would have A at position 3, Aat position 5, U at position 10, C at position 11, A and position 13, Aand position 14 and A at position 19. However, SEQ. ID NO. 0028 wouldalso have C at position −1, U at position −2 and G at position −3.

For a 19 base pair siRNA, an optimal sequence of one of the strands maybe represented below, where N is any base, A, C, G, or U:

SEQ. ID NO. 0001. NNANANNNNUCNAANNNNA SEQ. ID NO. 0002.NNANANNNNUGNAANNNNA SEQ. ID NO. 0003. NNANANNNNUUNAANNNNA SEQ. ID NO.0004. NNANANNNNUCNCANNNNA SEQ. ID NO. 0005. NNANANNNNUGNCANNNNA SEQ. IDNO. 0006. NNANANNNNUUNCANNNNA SEQ. ID NO. 0007. NNANANNNNUCNUANNNNA SEQ.ID NO. 0008. NNANANNNNUGNUANNNNA SEQ. ID NO. 0009. NNANANNNNUUNUANNNNASEQ. ID NO. 0010. NNANCNNNNUCNAANNNNA SEQ. ID NO. 0011.NNANCNNNNUGNAANNNNA SEQ. ID NO. 0012. NNANCNNNNUUNAANNNNA SEQ. ID NO.0013. NNANCNNNNUCNCANNNNA SEQ. ID NO. 0014. NNANCNNNNUGNCANNNNA SEQ. IDNO. 0015. NNANCNNNNUUNCANNNNA SEQ. ID NO. 0016. NNANCNNNNUCNUANNNNA SEQ.ID NO. 0017. NNANCNNNNUGNUANNNNA SEQ. ID NO. 0018. NNANCNNNNUUNUANNNNASEQ. ID NO. 0019. NNANGNNNNUCNAANNNNA SEQ. ID NO. 0020.NNANGNNNNUGNAANNNNA SEQ. ID NO. 0021. NNANGNNNNUUNAANNNNA SEQ. ID NO.0022. NNANGNNNNUCNCANNNNA SEQ. ID NO. 0023. NNANGNNNNUGNCANNNNA SEQ. IDNO. 0024. NNANGNNNNUUNCANNNNA SEQ. ID NO. 0025. NNANGNNNNUCNUANNNNA SEQ.ID NO. 0026. NNANGNNNNUGNUANNNNA SEQ. ID NO. 0027. NNANGNNNNNUNUANNNNA

In one embodiment, the sequence used as an siRNA is selected by choosingthe siRNA that score highest according to one of the following sevenalgorithms that are represented by Formulas I-VII:

Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm _(20° C.))*3−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula I

Relative functionality of siRNA=−(GC/3)−(AU ₁₅₋₁₉)*3−(G ₁₃)*3−(C ₁₉)+(A₁₉)*2+(A ₃)  Formula II

Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm_(20° C.))*3  Formula III

Relative functionality of siRNA=−GC/2+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*2−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IV

Relative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula V

Relative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)  FormulaVI

Relative functionality of siRNA=−(GC/2)+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*1−(G₁₃)*3−(C ₁₉)+(A ₁₉)*3+(A ₃)*3+(U ₁₀)/2+(A ₁₄)/2−(U ₅)/2−(A₁₁)/2  Formula VII

In Formulas I-VII:

wherein A₁₉=1 if A is the base at position 19 on the sense strand,otherwise its value is 0,

AU₁₅₋₁₉=0-5 depending on the number of A or U bases on the sense strandat positions 15-19;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise itsvalue is 0;

GC=the number of G and C bases in the entire sense strand;

Tm_(20° C.)=1 if the Tm is greater than 20° C.;

A₃=1 if A is the base at position 3 on the sense strand, otherwise itsvalue is 0;U₁₀=1 if U is the base at position 10 on the sense strand, otherwise itsvalue is 0;A₁₄=1 if A is the base at position 14 on the sense strand, otherwise itsvalue is 0;U₅=1 if U is the base at position 5 on the sense strand, otherwise itsvalue is 0; andA₁₁=1 if A is the base at position 11 of the sense strand, otherwise itsvalue is 0.

Formulas I-VII provide relative information regarding functionality.When the values for two sequences are compared for a given formula, therelative functionality is ascertained; a higher positive numberindicates a greater functionality. For example, in many applications avalue of 5 or greater is beneficial.

Additionally, in many applications, more than one of these formulaswould provide useful information as to the relative functionality ofpotential siRNA sequences. However, it is beneficial to have more thanone type of formula, because not every formula will be able to help todifferentiate among potential siRNA sequences. For example, inparticularly high GC mRNAs, formulas that take that parameter intoaccount would not be useful and application of formulas that lack GCelements (e.g., formulas V and VI) might provide greater insights intoduplex functionality. Similarly, formula II might by used in situationswhere hairpin structures are not observed in duplexes, and formula IVmight be applicable for sequences that have higher AU content. Thus, onemay consider a particular sequence in light of more than one or even allof these algorithms to obtain the best differentiation among sequences.In some instances, application of a given algorithm may identify anunusually large number of potential siRNA sequences, and in those cases,it may be appropriate to re-analyze that sequence with a secondalgorithm that is, for instance, more stringent. Alternatively, it isconceivable that analysis of a sequence with a given formula yields noacceptable siRNA sequences (i.e. low SMARTSCORES™, or siRNA ranking). Inthis instance, it may be appropriate to re-analyze that sequences with asecond algorithm that is, for instance, less stringent. In still otherinstances, analysis of a single sequence with two separate formulas maygive rise to conflicting results (i.e. one formula generates a set ofsiRNA with high SMARTSCORES™, or siRNA ranking, while the other formulaidentifies a set of siRNA with low SMARTSCORES™, or siRNA ranking). Inthese instances, it may be necessary to determine which weightedfactor(s) (e.g. GC content) are contributing to the discrepancy andassessing the sequence to decide whether these factors should or shouldnot be included. Alternatively, the sequence could be analyzed by athird, fourth, or fifth algorithm to identify a set of rationallydesigned siRNA.

The above-referenced criteria are particularly advantageous when used incombination with pooling techniques as depicted in Table I:

TABLE I FUNCTIONAL PROBABILITY OLIGOS POOLS CRITERIA >95% >80%<70% >95% >80% <70% CURRENT 33.0 50.0 23.0 79.5 97.3 0.3 NEW 50.0 88.58.0 93.8 99.98 0.005 (GC) 28.0 58.9 36.0 72.8 97.1 1.6

The term “current” used in Table I refers to Tuschl's conventional siRNAparameters (Elbashir, S. M. et al. (2002) “Analysis of gene function insomatic mammalian cells using small interfering RNAs” Methods 26:199-213). “New” refers to the design parameters described in FormulasI-VII. “GC” refers to criteria that select siRNA solely on the basis ofGC content.

As Table I indicates, when more functional siRNA duplexes are chosen,siRNAs that produce<70% silencing drops from 23% to 8% and the number ofsiRNA duplexes that produce>80% silencing rises from 50% to 88.5%.Further, of the siRNA duplexes with >80% silencing, a larger portion ofthese siRNAs actually silence>95% of the target expression (the newcriteria increases the portion from 33% to 50%). Using this new criteriain pooled siRNAs, shows that, with pooling, the amount of silencing>95%increases from 79.5% to 93.8% and essentially eliminates any siRNA poolfrom silencing less than 70%.

Table II similarly shows the particularly beneficial results of poolingin combination with the aforementioned criteria. However, Table II,which takes into account each of the aforementioned variables,demonstrates even a greater degree of improvement in functionality.

TABLE II FUNCTIONAL PROBABILITY OLIGOS POOLS NON- NON- FUNCTIONALAVERAGE FUNCTIONAL FUNCTIONAL AVERAGE FUNCTIONAL RANDOM 20 40 50 67 97 3CRITERIA 1 52 99 0.1 97 93 0.0040 CRITERIA 4 89 99 0.1 99 99 0.0000

The terms “functional,” “Average,” and “Non-functional” used in TableII, refer to siRNA that exhibit>80%, >50%, and <50% functionality,respectively. Criteria 1 and 4 refer to specific criteria describedabove.

The above-described algorithms may be used with or without a computerprogram that allows for the inputting of the sequence of the mRNA andautomatically outputs the optimal siRNA. The computer program may, forexample, be accessible from a local terminal or personal computer, overan internal network or over the Internet.

In addition to the formulas above, more detailed algorithms may be usedfor selecting siRNA. Preferably, at least one RNA duplex of 18-30 basepairs is selected such that it is optimized according a formula selectedfrom:

$\begin{matrix}{{{\left( {- 14} \right)*G_{13}} - {13*A_{1}} - {12*U_{7}} - {11*U_{2}} - {10*A_{11}} - {10*U_{4}} - {10*C_{3}} - {10*C_{5}} - {10*C_{6}} - {9*A_{10}} - {9*U_{9}} - {9*C_{18}} - {8*G_{10}} - {7*U_{1}} - {7*U_{16}} - {7*C_{17}} - {7*C_{19}} + {7*U_{17}} + {8*A_{2}} + {8*A_{4}} + {8*A_{5}} + {8*C_{4}} + {9*G_{8}} + {10*A_{7}} + {10*U_{18}} + {11*A_{19}} + {11*C_{9}} + {15*G_{1}} + {18*A_{3}} + {19*U_{10}} - {Tm} - {3*\left( {GC}_{total} \right)} - {6*\left( {GC}_{15\text{-}19} \right)} - {30*X}};{and}} & {{Formula}\mspace{14mu} {VIII}} \\{{{(14.1)*A_{3}} + {(14.0)*A_{6}} + {(17.6)*A_{13}} + {(24.7)*A_{19}} + {(14.2)*U_{10}} + {(10.5)*C_{9}} + {(23.0)*G_{1}} + {(16.3)*G_{2}} + {\left( {- 12.3} \right)*A_{11}} + {\left( {- 19.3} \right)*U_{1}} + {\left( {- 12.1} \right)*U_{2}} + {\left( {- 11} \right)*U_{3}} + {\left( {- 15.2} \right)*U_{15}} + {\left( {- 11.3} \right)*U_{16}} + {\left( {- 11.8} \right)*C_{3}} + {\left( {- 17.4} \right)*C_{6}} + {\left( {- 10.5} \right)*C_{7}} + {\left( {- 13.7} \right)*G_{13}} + {\left( {- 25.9} \right)*G_{19}} - {Tm} - {3*\left( {GC}_{total} \right)} - {6*\left( {GC}_{15\text{-}19} \right)} - {30*X}};{and}} & {{Formula}\mspace{14mu} {IX}} \\{{\left( {- 8} \right)*A\; 1} + {\left( {- 1} \right)*A\; 2} + {(12)*A\; 3} + {(7)*A\; 4} + {(18)*A\; 5} + {(12)*A\; 6} + {(19)*A\; 7} + {(6)*A\; 8} + {\left( {- 4} \right)*A\; 9} + {\left( {- 5} \right)*A\; 10} + {\left( {- 2} \right)*A\; 11} + {\left( {- 5} \right)*A\; 12} + {(17)*A\; 13} + {\left( {- 3} \right)*A\; 14} + {(4)*A\; 15} + {(2)*A\; 16} + {(8)*A\; 17} + {(11)*A\; 18} + {(30)*A\; 19} + {\left( {- 13} \right)*U\; 1} + {\left( {- 10} \right)*U\; 2} + {(2)*U\; 3} + {\left( {- 2} \right)*U\; 4} + {\left( {- 5} \right)*U\; 5} + {(5)*U\; 6} + {\left( {- 2} \right)*U\; 7} + {\left( {- 10} \right)*U\; 8} + {\left( {- 5} \right)*U\; 9} + {(15)*U\; 10} + {\left( {- 1} \right)*U\; 11} + {(0)*U\; 12} + {(10)*U\; 13} + {\left( {- 9} \right)*U\; 14} + {\left( {- 13} \right)*U\; 15} + {\left( {- 10} \right)*U\; 16} + {(3)*U\; 17} + {(9)*U\; 18} + {(9)*U\; 19} + {(7)*C\; 1} + {(3)*C\; 2} + {\left( {- 21} \right)*C\; 3} + {(5)*C\; 4} + {\left( {- 9} \right)*C\; 5} + {\left( {- 20} \right)*C\; 6} + {\left( {- 18} \right)*C\; 7} + {\left( {- 5} \right)*C\; 8} + {(5)*C\; 9} + {(1)*C\; 10} + {(2)*C\; 11} + {\left( {- 5} \right)*C\; 12} + {\left( {- 3} \right)*C\; 13} + {\left( {- 6} \right)*C\; 14} + {\left( {- 2} \right)*C\; 15} + {\left( {- 5} \right)*C\; 16} + {\left( {- 3} \right)*C\; 17} + {\left( {- 12} \right)*C\; 18} + {\left( {- 18} \right)*C\; 19} + {(14)*G\; 1} + {(8)*G\; 2} + {(7)*G\; 3} + {\left( {- 10} \right)*G\; 4} + {\left( {- 4} \right)*G\; 5} + {(2)*G\; 6} + {(1)*G\; 7} + {(9)*G\; 8} + {(5)*G\; 9} + {\left( {- 11} \right)*G\; 10} + {(1)*G\; 11} + {(9)*G\; 12} + {\left( {- 24} \right)*G\; 13} + {(18)*G\; 14} + {(11)*G\; 15} + {(13)*G\; 16} + {\left( {- 7} \right)*G\; 17} + {\left( {- 9} \right)*G\; 18} + {\left( {- 22} \right)*G\; 19} + {6*\left( {{{number}\mspace{14mu} {of}\mspace{14mu} A} + {U\mspace{14mu} {in}\mspace{14mu} {position}\mspace{14mu} 15\text{-}19}} \right)} - {3*\left( {{{number}\mspace{14mu} {of}{\mspace{11mu} \;}G} + {C\mspace{14mu} {in}\mspace{14mu} {whole}\mspace{14mu} {siRNA}}} \right)}} & {{Formula}\mspace{14mu} X}\end{matrix}$

wherein

A₁=1 if A is the base at position 1 of the sense strand, otherwise itsvalue is 0;

A₂=1 if A is the base at position 2 of the sense strand, otherwise itsvalue is 0;A₃=1 if A is the base at position 3 of the sense strand, otherwise itsvalue is 0;A₄=1 if A is the base at position 4 of the sense strand, otherwise itsvalue is 0;A₅=1 if A is the base at position 5 of the sense strand, otherwise itsvalue is 0;A₆=1 if A is the base at position 6 of the sense strand, otherwise itsvalue is 0;A₇=1 if A is the base at position 7 of the sense strand, otherwise itsvalue is 0;A₁₀=1 if A is the base at position 10 of the sense strand, otherwise itsvalue is 0;A₁₁=1 if A is the base at position 11 of the sense strand, otherwise itsvalue is 0;A₁₃=1 if A is the base at position 13 of the sense strand, otherwise itsvalue is 0;A₁₉=1 if A is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;

C₃=1 if C is the base at position 3 of the sense strand, otherwise itsvalue is 0;

C₄=1 if C is the base at position 4 of the sense strand, otherwise itsvalue is 0;C₅=1 if C is the base at position 5 of the sense strand, otherwise itsvalue is 0;C₆=1 if C is the base at position 6 of the sense strand, otherwise itsvalue is 0;C₇=1 if C is the base at position 7 of the sense strand, otherwise itsvalue is 0;C₉=1 if C is the base at position 9 of the sense strand, otherwise itsvalue is 0;C₁₇=1 if C is the base at position 17 of the sense strand, otherwise itsvalue is 0;C₁₈=1 if C is the base at position 18 of the sense strand, otherwise itsvalue is 0;C₁₉=1 if C is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;

G₁=1 if G is the base at position 1 on the sense strand, otherwise itsvalue is 0;

G₂=1 if G is the base at position 2 of the sense strand, otherwise itsvalue is 0;G₈=1 if G is the base at position 8 on the sense strand, otherwise itsvalue is 0;G₁₀=1 if G is the base at position 10 on the sense strand, otherwise itsvalue is 0;G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;G₁₉=1 if G is the base at position 19 of the sense strand, otherwise ifanother base is present or the sense strand is only 18 base pairs inlength, its value is 0;

U₁=1 if U is the base at position 1 on the sense strand, otherwise itsvalue is 0;

U₂=1 if U is the base at position 2 on the sense strand, otherwise itsvalue is 0;U₃=1 if U is the base at position 3 on the sense strand, otherwise itsvalue is 0;U₄=1 if U is the base at position 4 on the sense strand, otherwise itsvalue is 0;U₇=1 if U is the base at position 7 on the sense strand, otherwise itsvalue is 0;U₉=1 if U is the base at position 9 on the sense strand, otherwise itsvalue is 0;U₁₀=1 if U is the base at position 10 on the sense strand, otherwise itsvalue is 0;U₁₅=1 if U is the base at position 15 on the sense strand, otherwise itsvalue is 0;U₁₆=1 if U is the base at position 16 on the sense strand, otherwise itsvalue is 0;U₁₇=1 if U is the base at position 17 on the sense strand, otherwise itsvalue is 0;U₁₈=1 if U is the base at position 18 on the sense strand, otherwise itsvalue is 0;

GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of the sensestrand, or within positions 15-18 if the sense strand is only 18 basepairs in length;

GC_(total)=the number of G and C bases in the sense strand;

Tm=100 if the siRNA oligo has the internal repeat longer then 4 basepairs, otherwise its value is 0; and

X=the number of times that the same nucleotide repeats four or moretimes in a row.

The above formulas VIII, IX, and X, as well as formulas I-VII, providemethods for selecting siRNA in order to increase the efficiency of genesilencing. A subset of variables of any of the formulas may be used,though when fewer variables are used, the optimization hierarchy becomesless reliable.

With respect to the variables of the above-referenced formulas, a singleletter of A or C or G or U followed by a subscript refers to a binarycondition. The binary condition is that either the particular base ispresent at that particular position (wherein the value is “1”) or thebase is not present (wherein the value is “0”). Because position 19 isoptional, i.e., there might be only 18 base pairs, when there are only18 base pairs, any base with a subscript of 19 in the formulas abovewould have a zero value for that parameter. Before or after eachvariable is a number followed by *, which indicates that the value ofthe variable is to be multiplied or weighed by that number.

The numbers preceding the variables A, or G, or C, or U in FormulasVIII, IX, and X (or after the variables in Formula I-VII) weredetermined by comparing the difference in the frequency of individualbases at different positions in functional siRNA and total siRNA.Specifically, the frequency in which a given base was observed at aparticular position in functional groups was compared with the frequencythat that same base was observed in the total, randomly selected siRNAset. If the absolute value of the difference between the functional andtotal values was found to be greater than 6%, that parameter wasincluded in the equation. Thus, for instance, if the frequency offinding a “G” at position 13 (GC₁₃) is found to be 6% in a givenfunctional group, and the frequency of G₁₃ in the total population ofsiRNAs is 20%, the difference between the two values is 6%-20%=−14%. Asthe absolute value is greater than six (6), this factor (−14) isincluded in the equation. Thus, in Formula VIII, in cases where thesiRNA under study has a G in position 13, the accrued value is(−14)*(1)=−14. In contrast, when a base other than G is found atposition 13, the accrued value is (−14)*(O)=0.

When developing a means to optimize siRNAs, the inventors observed thata bias toward low internal thermodynamic stability of the duplex at the5′-antisense (AS) end is characteristic of naturally occurring miRNAprecursors. The inventors extended this observation to siRNAs for whichfunctionality had been assessed in tissue culture.

With respect to the parameter GC₁₅₋₁₉, a value of 0-5 will be ascribeddepending on the number of G or C bases at positions 15 to 19. If thereare only 18 base pairs, the value is between 0 and 4.

With respect to the criterion GC_(total) content, a number from 0-30will be ascribed, which correlates to the total number of G and Cnucleotides on the sense strand, excluding overhangs. Without wishing tobe bound by any one theory, it is postulated that the significance ofthe GC content (as well as AU content at positions 15-19, which is aparameter for formulas III-VII) relates to the easement of the unwindingof a double-stranded siRNA duplex. Duplex unwinding is believed to becrucial for siRNA functionality in vivo and overall low internalstability, especially low internal stability of the first unwound basepair is believed to be important to maintain sufficient processivity ofRISC complex-induced duplex unwinding. If the duplex has 19 base pairs,those at positions 15-19 on the sense strand will unwind first if themolecule exhibits a sufficiently low internal stability at thatposition. As persons skilled in the art are aware, RISC is a complex ofapproximately twelve proteins; Dicer is one, but not the only, helicasewithin this complex. Accordingly, although the GC parameters arebelieved to relate to activity with Dicer, they are also important foractivity with other RISC proteins.

The value of the parameter Tm is 0 when there are no internal repeatslonger than (or equal to) four base pairs present in the siRNA duplex;otherwise the value is 1. Thus for example, if the sequence ACGUACGU, orany other four nucleotide (or more) palindrome exists within thestructure, the value will be one (1). Alternatively if the structureACGGACG, or any other 3 nucleotide (or less) palindrome exists, thevalue will be zero (0).

The variable “X” refers to the number of times that the same nucleotideoccurs contiguously in a stretch of four or more units. If there are,for example, four contiguous As in one part of the sequence andelsewhere in the sequence four contiguous Cs, X=2. Further, if there aretwo separate contiguous stretches of four of the same nucleotides oreight or more of the same nucleotides in a row, then X=2. However, Xdoes not increase for five, six or seven contiguous nucleotides.

Again, when applying Formula VIII, Formula IX, or Formula X, to a givenmRNA, (the “target RNA” or “target molecule”), one may use a computerprogram to evaluate the criteria for every sequence of 18-30 base pairsor only sequences of a fixed length, e.g., 19 base pairs. Preferably thecomputer program is designed such that it provides a report ranking ofall of the potential siRNAs 18-30 base pairs, ranked according to whichsequences generate the highest value. A higher value refers to a moreefficient siRNA for a particular target gene. The computer program thatmay be used may be developed in any computer language that is known tobe useful for scoring nucleotide sequences, or it may be developed withthe assistance of commercially available product such as Microsoft'sPRODUCT.NET. Additionally, rather than run every sequence through oneand/or another formula, one may compare a subset of the sequences, whichmay be desirable if for example only a subset are available. Forinstance, it may be desirable to first perform a BLAST (Basic LocalAlignment Search Tool) search and to identify sequences that have nohomology to other targets. Alternatively, it may be desirable to scanthe sequence and to identify regions of moderate GC context, thenperform relevant calculations using one of the above-described formulason these regions. These calculations can be done manually or with theaid of a computer.

As with Formulas I-VII, either Formula VIII, Formula IX, or Formula Xmay be used for a given mRNA target sequence. However, it is possiblethat according to one or the other formula more than one siRNA will havethe same value. Accordingly, it is beneficial to have a second formulaby which to differentiate sequences. Formulas IX and X were derived in asimilar fashion as Formula VIII, yet used a larger data set and thusyields sequences with higher statistical correlations to highlyfunctional duplexes. The sequence that has the highest value ascribed toit may be referred to as a “first optimized duplex.” The sequence thathas the second highest value ascribed to it may be referred to as a“second optimized duplex.” Similarly, the sequences that have the thirdand fourth highest values ascribed to them may be referred to as a thirdoptimized duplex and a fourth optimized duplex, respectively. When morethan one sequence has the same value, each of them may, for example, bereferred to as first optimized duplex sequences or co-first optimizedduplexes. Formula X is similar to Formula IX, yet uses a greater numbersof variables and for that reason, identifies sequences on the basis ofslightly different criteria.

It should also be noted that the output of a particular algorithm willdepend on several of variables including: (1) the size of the database(s) being analyzed by the algorithm, and (2) the number andstringency of the parameters being applied to screen each sequence.Thus, for example, in U.S. patent application Ser. No. 10/714,333,entitled “Functional and Hyperfunctional siRNA,” filed Nov. 14, 2003,Formula VIII was applied to the known human genome (NCBI REFSEQdatabase) through ENTREZ (EFETCH). As a result of these procedures,roughly 1.6 million siRNA sequences were identified. Application ofFormula VIII to the same database in March of 2004 yielded roughly 2.2million sequences, a difference of approximately 600,000 sequencesresulting from the growth of the database over the course of the monthsthat span this period of time. Application of other formulas (e.g.,Formula X) that change the emphasis of, include, or eliminate differentvariables can yield unequal numbers of siRNAs. Alternatively, in caseswhere application of one formula to one or more genes fails to yieldsufficient numbers of siRNAs with scores that would be indicative ofstrong silencing, said genes can be reassessed with a second algorithmthat is, for instance, less stringent.

siRNA sequences identified using Formula VIII and Formula X (minussequences generated by Formula VIII) are contained within the sequencelisting. The data included in the sequence listing is described morefully below. The sequences identified by Formula VIII and Formula X thatare disclosed in the sequence listing may be used in gene silencingapplications.

It should be noted that for Formulas VIII, IX, and X all of theaforementioned criteria are identified as positions on the sense strandwhen oriented in the 5′ to 3′ direction as they are identified inconnection with Formulas I-VII unless otherwise specified.

Formulas I-X, may be used to select or to evaluate one, or more thanone, siRNA in order to optimize silencing. Preferably, at least twooptimized siRNAs that have been selected according to at least one ofthese formulas are used to silence a gene, more preferably at leastthree and most preferably at least four. The siRNAs may be usedindividually or together in a pool or kit. Further, they may be appliedto a cell simultaneously or separately. Preferably, the at least twosiRNAs are applied simultaneously. Pools are particularly beneficial formany research applications. However, for therapeutics, it may be moredesirable to employ a single hyperfunctional siRNA as describedelsewhere in this application.

When planning to conduct gene silencing, and it is necessary to choosebetween two or more siRNAs, one should do so by comparing the relativevalues when the siRNA are subjected to one of the formulas above. Ingeneral a higher scored siRNA should be used.

Useful applications include, but are not limited to, target validation,gene functional analysis, research and drug discovery, gene therapy andtherapeutics. Methods for using siRNA in these applications are wellknown to persons of skill in the art.

Because the ability of siRNA to function is dependent on the sequence ofthe RNA and not the species into which it is introduced, the presentinvention is applicable across a broad range of species, including butnot limited to all mammalian species, such as humans, dogs, horses,cats, cows, mice, hamsters, chimpanzees and gorillas, as well as otherspecies and organisms such as bacteria, viruses, insects, plants and C.elegans.

The present invention is also applicable for use for silencing a broadrange of genes, including but not limited to the roughly 45,000 genes ofa human genome, and has particular relevance in cases where those genesare associated with diseases such as diabetes, Alzheimer's, cancer, aswell as all genes in the genomes of the aforementioned organisms.

The siRNA selected according to the aforementioned criteria or one ofthe aforementioned algorithms are also, for example, useful in thesimultaneous screening and functional analysis of multiple genes andgene families using high throughput strategies, as well as in directgene suppression or silencing.

Development of the Algorithms

To identify siRNA sequence features that promote functionality and toquantify the importance of certain currently accepted conventionalfactors-such as G/C content and target site accessibility—the inventorssynthesized an siRNA panel consisting of 270 siRNAs targeting threegenes, Human Cyclophilin, Firefly Luciferase, and Human DBI. In allthree cases, siRNAs were directed against specific regions of each gene.For Human Cyclophilin and Firefly Luciferase, ninety siRNAs weredirected against a 199 bp segment of each respective mRNA. For DBI, 90siRNAs were directed against a smaller, 109 base pair region of themRNA. The sequences to which the siRNAs were directed are providedbelow.

It should be noted that in certain sequences, “t” is present. This isbecause many databases contain information in this manner. However, thet denotes a uracil residue in mRNA and siRNA. Any algorithm will, unlessotherwise specified, process at in a sequence as a u.

Human cyclophilin: 193-390, M60857 SEQ. ID NO. 29: gttccaaaaa cagtggataattttgtggcc ttagctacag gagagaaagg atttggctac aaaaacagca aattccatcgtgtaatcaag gacttcatga tccagggcgg agacttcacc aggggagatg gcacaggaggaaagagcatc tacggtgagc gcttccccga tgagaacttc aaactgaagc actacgggcctggctggg Firefly luciferase: 1434-1631, U47298 (pGL3, Promega) SEQ. IDNO. 30: tgaacttccc gccgccgttg ttgttttgga gcacggaaag acgatgacggaaaaagagat cgtggattac gtcgccagtc aagtaacaac cgcgaaaaag ttgcgcggaggagttgtgtt tgtggacgaa gtaccgaaag gtcttaccgg aaaactcgac gcaagaaaaatcagagagat cctcataaag gccaagaagg DBI, NM_020548 (202-310) (everyposition) SEQ. ID NO. 0031: acgggcaagg ccaagtggga tgcctggaat gagctgaaagggacttccaa ggaagatgcc atgaaagctt acatcaacaa agtagaagag ctaaagaaaaaatacggg

A list of the siRNAs appears in Table III (see Examples Section, ExampleII)

The set of duplexes was analyzed to identify correlations between siRNAfunctionality and other biophysical or thermodynamic properties. Whenthe siRNA panel was analyzed in functional and non-functional subgroups,certain nucleotides were much more abundant at certain positions infunctional or non-functional groups. More specifically, the frequency ofeach nucleotide at each position in highly functional siRNA duplexes wascompared with that of nonfunctional duplexes in order to assess thepreference for or against any given nucleotide at every position. Theseanalyses were used to determine important criteria to be included in thesiRNA algorithms (Formulas VIII, IX, and X).

The data set was also analyzed for distinguishing biophysical propertiesof siRNAs in the functional group, such as optimal percent of GCcontent, propensity for internal structures and regional thermodynamicstability. Of the presented criteria, several are involved in duplexrecognition, RISC activation/duplex unwinding, and target cleavagecatalysis.

The original data set that was the source of the statistically derivedcriteria is shown in FIG. 2. Additionally, this figure shows that randomselection yields siRNA duplexes with unpredictable and widely varyingsilencing potencies as measured in tissue culture using HEK293 cells. Inthe figure, duplexes are plotted such that each x-axis tick-markrepresents an individual siRNA, with each subsequent siRNA differing intarget position by two nucleotides for Human Cyclophilin B and FireflyLuciferase, and by one nucleotide for Human DBI. Furthermore, the y-axisdenotes the level of target expression remaining after transfection ofthe duplex into cells and subsequent silencing of the target.

siRNA identified and optimized in this document work equally well in awide range of cell types. FIG. 3 a shows the evaluation of thirty siRNAstargeting the DBI gene in three cell lines derived from differenttissues. Each DBI siRNA displays very similar functionality in HEK293(ATCC, CRL-1573, human embryonic kidney), HeLa (ATCC, CCL-2, cervicalepithelial adenocarcinoma) and DU145 (HTB-81, prostate) cells asdetermined by the B-DNA assay. Thus, siRNA functionality is determinedby the primary sequence of the siRNA and not by the intracellularenvironment. Additionally, it should be noted that although the presentinvention provides for a determination of the functionality of siRNA fora given target, the same siRNA may silence more than one gene. Forexample, the complementary sequence of the silencing siRNA may bepresent in more than one gene. Accordingly, in these circumstances, itmay be desirable not to use the siRNA with highest SMARTSCORE™, or siRNAranking. In such circumstances, it may be desirable to use the siRNAwith the next highest SMARTSCORE™, or siRNA ranking.

To determine the relevance of G/C content in siRNA function, the G/Ccontent of each duplex in the panel was calculated and the functionalclasses of siRNAs (<F50, ≧F50, ≧F80, ≧F95 where F refers to the percentgene silencing) were sorted accordingly. The majority of thehighly-functional siRNAs (≧F95) fell within the G/C content range of36%-52% (FIG. 3B). Twice as many non-functional (<F50) duplexes fellwithin the high G/C content groups (>57% GC content) compared to the36%-52% group. The group with extremely low GC content (26% or less)contained a higher proportion of non-functional siRNAs and nohighly-functional siRNAs. The G/C content range of 30%-52% was thereforeselected as Criterion I for siRNA functionality, consistent with theobservation that a G/C range 30%-70% promotes efficient RNAi targeting.Application of this criterion alone provided only a marginal increase inthe probability of selecting functional siRNAs from the panel: selectionof F50 and F95 siRNAs was improved by 3.6% and 2.2%, respectively. ThesiRNA panel presented here permitted a more systematic analysis andquantification of the importance of this criterion than that usedpreviously.

A relative measure of local internal stability is the A/U base pair (bp)content; therefore, the frequency of A/U bp was determined for each ofthe five terminal positions of the duplex (5′ sense (S)/5′ antisense(AS)) of all siRNAs in the panel. Duplexes were then categorized by thenumber of A/U bp in positions 1-5 and 15-19 of the sense strand. Thethermodynamic flexibility of the duplex 5′-end (positions 1-5; S) didnot appear to correlate appreciably with silencing potency, while thatof the 3′-end (positions 15-19; S) correlated with efficient silencing.No duplexes lacking A/U bp in positions 15-19 were functional. Thepresence of one A/U bp in this region conferred some degree offunctionality, but the presence of three or more A/Us was preferable andtherefore defined as Criterion II. When applied to the test panel, onlya marginal increase in the probability of functional siRNA selection wasachieved: a 1.8% and 2.3% increase for F50 and F95 duplexes,respectively (Table IV).

The complementary strands of siRNAs that contain internal repeats orpalindromes may form internal fold-back structures. These hairpin-likestructures exist in equilibrium with the duplexed form effectivelyreducing the concentration of functional duplexes. The propensity toform internal hairpins and their relative stability can be estimated bypredicted melting temperatures. High Tm reflects a tendency to formhairpin structures. Lower Tm values indicate a lesser tendency to formhairpins. When the functional classes of siRNAs were sorted by T_(m)(FIG. 3 c), the following trends were identified: duplexes lackingstable internal repeats were the most potent silencers (no F95 duplexwith predicted hairpin structure T_(m)>60° C.). In contrast, about 60%of the duplexes in the groups having internal hairpins with calculatedT_(m) values less than 20° C. were F80. Thus, the stability of internalrepeats is inversely proportional to the silencing effect and definesCriterion III (predicted hairpin structure T_(m)≦20° C.).

Sequence-Based Determinants of siRNA Functionality

When the siRNA panel was sorted into functional and non-functionalgroups, the frequency of a specific nucleotide at each position in afunctional siRNA duplex was compared with that of a nonfunctional duplexin order to assess the preference for or against a certain nucleotide.FIG. 4 shows the results of these queries and the subsequent resortingof the data set (from FIG. 2). The data is separated into two sets:those duplexes that meet the criteria, a specific nucleotide in acertain position—grouped on the left (Selected) and those that donot—grouped on the right (Eliminated). The duplexes are further sortedfrom most functional to least functional with the y-axis of FIG. 4 a-erepresenting the % expression i.e., the amount of silencing that iselicited by the duplex (Note: each position on the X-axis represents adifferent duplex). Statistical analysis revealed correlations betweensilencing and several sequence-related properties of siRNAs. FIG. 4 andTable IV show quantitative analysis for the following fivesequence-related properties of siRNA: (A) an A at position 19 of thesense strand; (B) an A at position 3 of the sense strand; (C) a U atposition 10 of the sense strand; (D) a base other than G at position 13of the sense strand; and (E) a base other than C at position 19 of thesense strand.

When the siRNAs in the panel were evaluated for the presence of an A atposition 19 of the sense strand, the percentage of non-functionalduplexes decreased from 20% to 11.8%, and the percentage of F95 duplexesincreased from 21.7% to 29.4% (Table IV). Thus, the presence of an A inthis position defined Criterion IV.

Another sequence-related property correlated with silencing was thepresence of an A in position 3 of the sense strand (FIG. 4 b). Of thesiRNAs with A3, 34.4% were F95, compared with 21.7% randomly selectedsiRNAs. The presence of a U base in position 10 of the sense strandexhibited an even greater impact (FIG. 4 c). Of the duplexes in thisgroup, 41.7% were F95. These properties became criteria V and VI,respectively.

Two negative sequence-related criteria that were identified also appearon FIG. 4. The absence of a G at position 13 of the sense strand,conferred a marginal increase in selecting functional duplexes (FIG. 4d). Similarly, lack of a C at position 19 of the sense strand alsocorrelated with functionality (FIG. 4 e). Thus, among functionalduplexes, position 19 was most likely occupied by A, and rarely occupiedby C. These rules were defined as criteria VII and VIII, respectively.

Application of each criterion individually provided marginal butstatistically significant increases in the probability of selecting apotent siRNA. Although the results were informative, the inventorssought to maximize potency and therefore consider multiple criteria orparameters. Optimization is particularly important when developingtherapeutics. Interestingly, the probability of selecting a functionalsiRNA based on each thermodynamic criteria was 2%-4% higher than random,but 4%-8% higher for the sequence-related determinates. Presumably,these sequence-related increases reflect the complexity of the RNAimechanism and the multitude of protein-RNA interactions that areinvolved in RNAi-mediated silencing.

TABLE IV PERCENT IMPROVEMENT CRITERION FUNCTIONAL OVER RANDOM (%) I.30%-52% G/C Content <F50 16.4 −3.6 ≧F50 83.6 3.6 ≧F80 60.4 4.3 ≧F95 23.92.2 II. At least 3 A/U bases at <F50 18.2 −1.8 positions 15-19 of thesense ≧F50 81.8 1.8 strand ≧F80 59.7 3.6 ≧F95 24.0 2.3 III. Absence ofinternal <F50 16.7 −3.3 repeats, as measured by Tm of ≧F50 83.3 3.3secondary structure ≦20° C. ≧F80 61.1 5.0 ≧F95 24.6 2.9 IV. An A base atposition 19 <F50 11.8 −8.2 of the sense strand ≧F50 88.2 8.2 ≧F80 75.018.9 ≧F95 29.4 7.7 V. An A base at position 3 of <F50 17.2 −2.8 thesense strand ≧F50 82.8 2.8 ≧F80 62.5 6.4 ≧F95 34.4 12.7 VI. A U base atposition 10 <F50 13.9 −6.1 of the sense strand ≧F50 86.1 6.1 ≧F80 69.413.3 ≧F95 41.7 20 VII. A base other than C at <F50 18.8 −1.2 position 19of the sense strand ≧F50 81.2 1.2 ≧F80 59.7 3.6 ≧F95 24.2 2.5 VIII. Abase other than G at <F50 15.2 −4.8 position 13 of the sense strand ≧F5084.8 4.8 ≧F80 61.4 5.3 ≧F95 26.5 4.8The siRNA Selection Algorithm

In an effort to improve selection further, all identified criteria,including but not limited to those listed in Table IV were combined intothe algorithms embodied in Formula VIII, Formula IX, and Formula X. EachsiRNA was then assigned a score (referred to as a SMARTSCORE™, or siRNAranking) according to the values derived from the formulas. Duplexesthat scored higher than 0 or −20 (unadjusted), for Formulas VIII and IX,respectively, effectively selected a set of functional siRNAs andexcluded all non-functional siRNAs. Conversely, all duplexes scoringlower than 0 and −20 (minus 20) according to formulas VIII and IX,respectively, contained some functional siRNAs but included allnon-functional siRNAs. A graphical representation of this selection isshown in FIG. 5. It should be noted that the scores derived from thealgorithm can also be provided as “adjusted” scores. To convert FormulaVIII unadjusted scores into adjusted scores it is necessary to use thefollowing equation:

(160+unadjusted score)/2.25

When this takes place, an unadjusted score of “0” (zero) is converted to75. Similarly, unadjusted scores for Formula X can be converted toadjusted scores. In this instance, the following equation is applied:

(228+unadjusted score)/3.56

When these manipulations take place, an unadjusted score of 38 isconverted to an adjusted score of 75.

The methods for obtaining the seven criteria embodied in Table IV areillustrative of the results of the process used to develop theinformation for Formulas VIII, IX, and X. Thus similar techniques wereused to establish the other variables and their multipliers. Asdescribed above, basic statistical methods were use to determine therelative values for these multipliers.

To determine the value for “Improvement over Random” the difference inthe frequency of a given attribute (e.g., GC content, base preference)at a particular position is determined between individual functionalgroups (e.g., <F50) and the total siRNA population studied (e.g., 270siRNA molecules selected randomly). Thus, for instance, in Criterion 1(30%-52% GC content) members of the <F50 group were observed to have GCcontents between 30-52% in 16.4% of the cases. In contrast, the totalgroup of 270 siRNAs had GC contents in this range, 20% of the time. Thusfor this particular attribute, there is a small negative correlationbetween 30%-52% GC content and this functional group (i.e.,16.4%-20%=−3.6%). Similarly, for Criterion VI, (a “U” at position 10 ofthe sense strand), the >F95 group contained a “U” at this position 41.7%of the time. In contrast, the total group of 270 siRNAs had a “U” atthis position 21.7% of the time, thus the improvement over random iscalculated to be 20% (or 41.7%-21.7%).

Identifying The Average Internal Stability Profile of Strong siRNA

In order to identify an internal stability profile that ischaracteristic of strong siRNA, 270 different siRNAs derived from thecyclophilin B, the diazepam binding inhibitor (DBI), and the luciferasegene were individually transfected into HEK293 cells and tested fortheir ability to induce RNAi of the respective gene. Based on theirperformance in the in vivo assay, the sequences were then subdividedinto three groups, (i)>95% silencing; (ii) 80-95% silencing; and (iii)less than 50% silencing. Sequences exhibiting 51-84% silencing wereeliminated from further consideration to reduce the difficulties inidentifying relevant thermodynamic patterns.

Following the division of siRNA into three groups, a statisticalanalysis was performed on each member of each group to determine theaverage internal stability profile (AISP) of the siRNA. To accomplishthis the Oligo 5.0 Primer Analysis Software and other relatedstatistical packages (e.g., Excel) were exploited to determine theinternal stability of pentamers using the nearest neighbor methoddescribed by Freier et al., (1986) Improved free-energy parameters forpredictions of RNA duplex stability, Proc Natl. Acad. Sci. USA 83(24):9373-7. Values for each group at each position were then averaged, andthe resulting data were graphed on a linear coordinate system with theY-axis expressing the AG (free energy) values in kcal/mole and theX-axis identifying the position of the base relative to the 5′ end.

The results of the analysis identified multiple key regions in siRNAmolecules that were critical for successful gene silencing. At the3′-most end of the sense strand (5′antisense), highly functional siRNA(>95% gene silencing, see FIG. 6 a, >F95) have a low internal stability(AISP of position 19=˜−7.6 kcal/mol). In contrast low-efficiency siRNA(i.e., those exhibiting less than 50% silencing, <F50) display adistinctly different profile, having high ΔG values (˜−8.4 kcal/mol) forthe same position. Moving in a 5′ (sense strand) direction, the internalstability of highly efficient siRNA rises (position 12=˜=8.3 kcal/mole)and then drops again (position 7=˜−7.7 kcal/mol) before leveling off ata value of approximately −8.1 kcal/mol for the 5′ terminus. siRNA withpoor silencing capabilities show a distinctly different profile. Whilethe AISP value at position 12 is nearly identical with that of strongsiRNAs, the values at positions 7 and 8 rise considerably, peaking at ahigh of ˜−9.0 kcal/mol. In addition, at the 5′ end of the molecule theAISP profile of strong and weak siRNA differ dramatically. Unlike therelatively strong values exhibited by siRNA in the >95% silencing group,siRNAs that exhibit poor silencing activity have weak AISP values (−7.6,−7.5, and −7.5 kcal/mol for positions 1, 2 and 3 respectively).

Overall the profiles of both strong and weak siRNAs form distinctsinusoidal shapes that are roughly 180° out-of-phase with each other.While these thermodynamic descriptions define the archetypal profile ofa strong siRNA, it will likely be the case that neither the ΔG valuesgiven for key positions in the profile or the absolute position of theprofile along the Y-axis (i.e., the ΔG-axis) are absolutes. Profilesthat are shifted upward or downward (i.e., having on an average, higheror lower values at every position) but retain the relative shape andposition of the profile along the X-axis can be foreseen as beingequally effective as the model profile described here. Moreover, it islikely that siRNA that have strong or even stronger gene-specificsilencing effects might have exaggerated ΔG values (either higher orlower) at key positions. Thus, for instance, it is possible that the5′-most position of the sense strand (position 19) could have ΔG valuesof 7.4 kcal/mol or lower and still be a strong siRNA if, for instance, aG-C→G-T/U mismatch were substituted at position 19 and altered duplexstability. Similarly, position 12 and position 7 could have values above8.3 kcal/mol and below 7.7 kcal/mole, respectively, without abating thesilencing effectiveness of the molecule. Thus, for instance, at position12, a stabilizing chemical modification (e.g., a chemical modificationof the 2′ position of the sugar backbone) could be added that increasesthe average internal stability at that position. Similarly, at position7, mismatches similar to those described previously could be introducedthat would lower the ΔG values at that position.

Lastly, it is important to note that while functional and non-functionalsiRNA were originally defined as those molecules having specificsilencing properties, both broader or more limiting parameters can beused to define these molecules. As used herein, unless otherwisespecified, “non-functional siRNA” are defined as those siRNA that induceless than 50% (<50%) target silencing, “semi-functional siRNA” induce50-79% target silencing, “functional siRNA” are molecules that induce80-95% gene silencing, and “highly-functional siRNA” are molecules thatinduce great than 95% gene silencing. These definitions are not intendedto be rigid and can vary depending upon the design and needs of theapplication. For instance, it is possible that a researcher attemptingto map a gene to a chromosome using a functional assay, may identify ansiRNA that reduces gene activity by only 30%. While this level of genesilencing may be “non-functional” for, e.g., therapeutic needs, it issufficient for gene mapping purposes and is, under these uses andconditions, “functional.” For these reasons, functional siRNA can bedefined as those molecules having greater than 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, or 90% silencing capabilities at 100 nM transfectionconditions. Similarly, depending upon the needs of the study and/orapplication, non-functional and semi-functional siRNA can be defined ashaving different parameters. For instance, semi-functional siRNA can bedefined as being those molecules that induce 20%, 30%, 40%, 50%, 60%, or70% silencing at 100 nM transfection conditions. Similarly,non-functional siRNA can be defined as being those molecules thatsilence gene expression by less than 70%, 60%, 50%, 40%, 30%, or less.Nonetheless, unless otherwise stated, the descriptions stated in the“Definitions” section of this text should be applied.

Functional attributes can be assigned to each of the key positions inthe AISP of strong siRNA. The low 5′ (sense strand) AISP values ofstrong siRNAs may be necessary for determining which end of the moleculeenters the RISC complex. In contrast, the high and low AISP valuesobserved in the central regions of the molecule may be critical forsiRNA-target mRNA interactions and product release, respectively.

If the AISP values described above accurately define the thermodynamicparameters of strong siRNA, it would be expected that similar patternswould be observed in strong siRNA isolated from nature. Natural siRNAsexist in a harsh, RNase-rich environment and it can be hypothesized thatonly those siRNA that exhibit heightened affinity for RISC (i.e., siRNAthat exhibit an average internal stability profile similar to thoseobserved in strong siRNA) would survive in an intracellular environment.This hypothesis was tested using GFP-specific siRNA isolated from N.benthamiana. Llave et al. (2002) Endogenous and Silencing-AssociatedSmall RNAs in Plants, The Plant Cell 14, 1605-1619, introduced longdouble-stranded GFP-encoding RNA into plants and subsequentlyre-isolated GFP-specific siRNA from the tissues. The AISP of fifty-nineof these GFP-siRNA were determined, averaged, and subsequently plottedalongside the AISP profile obtained from the cyclophilinB/DBI/luciferase siRNA having >90% silencing properties (FIG. 6 b).Comparison of the two groups show that profiles are nearly identical.This finding validates the information provided by the internalstability profiles and demonstrates that: (1) the profile identified byanalysis of the cyclophilin B/DBI/luciferase siRNAs are not genespecific; and (2) AISP values can be used to search for strong siRNAs ina variety of species.

Both chemical modifications and base-pair mismatches can be incorporatedinto siRNA to alter the duplex's AISP and functionality. For instance,introduction of mismatches at positions 1 or 2 of the sense stranddestabilized the 5′ end of the sense strand and increases thefunctionality of the molecule (see Luc, FIG. 7). Similarly, addition of2′-O-methyl groups to positions 1 and 2 of the sense strand can alsoalter the AISP and (as a result) increase both the functionality of themolecule and eliminate off-target effects that results from sense strandhomology with the unrelated targets (FIGS. 8 a, 8 b).

Rationale for Criteria in a Biological Context

The fate of siRNA in the RNAi pathway may be described in 5 major steps:(1) duplex recognition and pre-RISC complex formation; (2) ATP-dependentduplex unwinding/strand selection and RISC activation; (3) mRNA targetidentification; (4) mRNA cleavage, and (5) product release (FIG. 1).Given the level of nucleic acid-protein interactions at each step, siRNAfunctionality is likely influenced by specific biophysical and molecularproperties that promote efficient interactions within the context of themulti-component complexes. Indeed, the systematic analysis of the siRNAtest set identified multiple factors that correlate well withfunctionality. When combined into a single algorithm, they proved to bevery effective in selecting active siRNAs.

The factors described here may also be predictive of key functionalassociations important for each step in RNAi. For example, the potentialformation of internal hairpin structures correlated negatively withsiRNA functionality. Complementary strands with stable internal repeatsare more likely to exist as stable hairpins thus decreasing theeffective concentration of the functional duplex form. This suggeststhat the duplex is the preferred conformation for initial pre-RISCassociation. Indeed, although single complementary strands can inducegene silencing, the effective concentration required is at least twoorders of magnitude higher than that of the duplex form.

siRNA-pre-RISC complex formation is followed by an ATP-dependent duplexunwinding step and “activation” of the RISC. The siRNA functionality wasshown to correlate with overall low internal stability of the duplex andlow internal stability of the 3′ sense end (or differential internalstability of the 3′ sense compare to the 5′ sense strand), which mayreflect strand selection and entry into the RISC. Overall duplexstability and low internal stability at the 3′ end of the sense strandwere also correlated with siRNA functionality. Interestingly, siRNAswith very high and very low overall stability profiles correlatestrongly with non-functional duplexes. One interpretation is that highinternal stability prevents efficient unwinding while very low stabilityreduces siRNA target affinity and subsequent mRNA cleavage by the RISC.

Several criteria describe base preferences at specific positions of thesense strand and are even more intriguing when considering theirpotential mechanistic roles in target recognition and mRNA cleavage.Base preferences for A at position 19 of the sense strand but not C, areparticularly interesting because they reflect the same base preferencesobserved for naturally occurring miRNA precursors. That is, among thereported miRNA precursor sequences 75% contain a U at position 1 whichcorresponds to an A in position 19 of the sense strand of siRNAs, whileG was under-represented in this same position for miRNA precursors.These observations support the hypothesis that both miRNA precursors andsiRNA duplexes are processed by very similar if not identical proteinmachinery. The functional interpretation of the predominance of a U/Abase pair is that it promotes flexibility at the 5′antisense ends ofboth siRNA duplexes and miRNA precursors and facilitates efficientunwinding and selective strand entrance into an activated RISC.

Among the criteria associated with base preferences that are likely toinfluence mRNA cleavage or possibly product release, the preference forU at position 10 of the sense strand exhibited the greatest impact,enhancing the probability of selecting an F80 sequence by 13.3%.Activated RISC preferentially cleaves target mRNA between nucleotides 10and 11 relative to the 5′ end of the complementary targeting strand.Therefore, it may be that U, the preferred base for mostendoribonucleases, at this position supports more efficient cleavage.Alternatively, a U/A bp between the targeting siRNA strand and itscognate target mRNA may create an optimal conformation for theRISC-associated “slicing” activity.

Post Algorithm Filters

According to another embodiment, the output of any one of the formulaspreviously listed can be filtered to remove or select for siRNAscontaining undesirable or desirable motifs or properties, respectively.In one example, sequences identified by any of the formulas can befiltered to remove any and all sequences that induce toxicity orcellular stress. Introduction of an siRNA containing a toxic motif intoa cell can induce cellular stress and/or cell death (apoptosis) which inturn can mislead researchers into associating a particular (e.g.,nonessential) gene with, e.g., an essential function. Alternatively,sequences generated by any of the before mentioned formulas can befiltered to identify and retain duplexes that contain toxic motifs. Suchduplexes may be valuable from a variety of perspectives including, forinstance, uses as therapeutic molecules. A variety of toxic motifs existand can exert their influence on the cell through RNAi and non-RNAipathways. Examples of toxic motifs are explained more fully in commonlyassigned U.S. Provisional Patent Application Ser. No. 60/538,874,entitled “Identification of Toxic Sequences,” filed Jan. 23, 2004.Briefly, toxic motifs include A/G UUU A/G/U, G/C AAA G/C, and GCCA, or acomplement of any of the foregoing.

In another instance, sequences identified by any of the before mentionedformulas can be filtered to identify duplexes that contain motifs (orgeneral properties) that provide serum stability or induce seruminstability. In one envisioned application of siRNA as therapeuticmolecules, duplexes targeting disease-associated genes will beintroduced into patients intravenously. As the half-life of single anddouble stranded RNA in serum is short, post-algorithm filters designedto select molecules that contain motifs that enhance duplex stability inthe presence of serum and/or (conversely) eliminate duplexes thatcontain motifs that destabilize siRNA in the presence of serum, would bebeneficial.

In another instance, sequences identified by any of the before mentionedformulas can be filtered to identify duplexes that are hyperfunctional.Hyperfunctional sequences are defined as those sequences that (1) inducegreater than 95% silencing of a specific target when they aretransfected at subnanomolar concentrations (i.e., less than onenanomolar); and/or (2) induce functional (or better) levels of silencingfor greater than 96 hours. Filters that identify hyperfunctionalmolecules can vary widely. In one example, the top ten, twenty, thirty,or forty siRNA can be assessed for the ability to silence a given targetat, e.g., concentrations of 1 nM and 0.5 nM to identify hyperfunctionalmolecules.

Pooling

According to another embodiment, the present invention provides a poolof at least two siRNAs, preferably in the form of a kit or therapeuticreagent, wherein one strand of each of the siRNAs, the sense strandcomprises a sequence that is substantially similar to a sequence withina target mRNA. The opposite strand, the antisense strand, willpreferably comprise a sequence that is substantially complementary tothat of the target mRNA. More preferably, one strand of each siRNA willcomprise a sequence that is identical to a sequence that is contained inthe target mRNA. Most preferably, each siRNA will be 19 base pairs inlength, and one strand of each of the siRNAs will be 100% complementaryto a portion of the target mRNA.

By increasing the number of siRNAs directed to a particular target usinga pool or kit, one is able both to increase the likelihood that at leastone siRNA with satisfactory functionality will be included, as well asto benefit from additive or synergistic effects. Further, when two ormore siRNAs directed against a single gene do not have satisfactorylevels of functionality alone, if combined, they may satisfactorilypromote degradation of the target messenger RNA and successfully inhibittranslation. By including multiple siRNAs in the system, not only is theprobability of silencing increased, but the economics of operation arealso improved when compared to adding different siRNAs sequentially.This effect is contrary to the conventional wisdom that the concurrentuse of multiple siRNA will negatively impact gene silencing (e.g.,Holen, T. et al. (2003) Similar behavior of single strand and doublestrand siRNAs suggests they act through a common RNAi pathway. NAR 31:2401-21407).

In fact, when two siRNAs were pooled together, 54% of the pools of twosiRNAs induced more than 95% gene silencing. Thus, a 2.5-fold increasein the percentage of functionality was achieved by randomly combiningtwo siRNAs. Further, over 84% of pools containing two siRNAs inducedmore than 80% gene silencing.

More preferably, the kit is comprised of at least three siRNAs, whereinone strand of each siRNA comprises a sequence that is substantiallysimilar to a sequence of the target mRNA and the other strand comprisesa sequence that is substantially complementary to the region of thetarget mRNA. As with the kit that comprises at least two siRNAs, morepreferably one strand will comprise a sequence that is identical to asequence that is contained in the mRNA and another strand that is 100%complementary to a sequence that is contained in the mRNA. Duringexperiments, when three siRNAs were combined together, 60% of the poolsinduced more than 95% gene silencing and 92% of the pools induced morethan 80% gene silencing.

Further, even more preferably, the kit is comprised of at least foursiRNAs, wherein one strand of each siRNA comprises a sequence that issubstantially similar to a region of the sequence of the target mRNA,and the other strand comprises a sequence that is substantiallycomplementary to the region of the target mRNA. As with the kit or poolthat comprises at least two siRNAs, more preferably one strand of eachof the siRNA duplexes will comprise a sequence that is identical to asequence that is contained in the mRNA, and another strand that is 100%complementary to a sequence that is contained in the mRNA.

Additionally, kits and pools with at least five, at least six, and atleast seven siRNAs may also be useful with the present invention. Forexample, pools of five siRNA induced 95% gene silencing with 77%probability and 80% silencing with 98.8% probability. Thus, pooling ofsiRNAs together can result in the creation of a target-specificsilencing reagent with almost a 99% probability of being functional. Thefact that such high levels of success are achievable using such pools ofsiRNA, enables one to dispense with costly and time-consumingtarget-specific validation procedures.

For this embodiment, as well as the other aforementioned embodiments,each of the siRNAs within a pool will preferably comprise 18-30 basepairs, more preferably 18-25 base pairs, and most preferably 19 basepairs. Within each siRNA, preferably at least 18 contiguous bases of theantisense strand will be 100% complementary to the target mRNA. Morepreferably, at least 19 contiguous bases of the antisense strand will be100% complementary to the target mRNA. Additionally, there may beoverhangs on either the sense strand or the antisense strand, and theseoverhangs may be at either the 5′ end or the 3′ end of either of thestrands, for example there may be one or more overhangs of 1-6 bases.When overhangs are present, they are not included in the calculation ofthe number of base pairs. The two nucleotide 3′ overhangs mimic naturalsiRNAs and are commonly used but are not essential. Preferably, theoverhangs should consist of two nucleotides, most often dTdT or UU atthe 3′ end of the sense and antisense strand that are not complementaryto the target sequence. The siRNAs may be produced by any method that isnow known or that comes to be known for synthesizing double stranded RNAthat one skilled in the art would appreciate would be useful in thepresent invention. Preferably, the siRNAs will be produced byDharmacon's proprietary ACE® technology. However, other methods forsynthesizing siRNAs are well known to persons skilled in the art andinclude, but are not limited to, any chemical synthesis of RNAoligonucleotides, ligation of shorter oligonucleotides, in vitrotranscription of RNA oligonucleotides, the use of vectors for expressionwithin cells, recombinant Dicer products and PCR products.

The siRNA duplexes within the aforementioned pools of siRNAs maycorrespond to overlapping sequences within a particular mRNA, ornon-overlapping sequences of the mRNA. However, preferably theycorrespond to non-overlapping sequences. Further, each siRNA may beselected randomly, or one or more of the siRNA may be selected accordingto the criteria discussed above for maximizing the effectiveness ofsiRNA.

Included in the definition of siRNAs are siRNAs that contain substitutedand/or labeled nucleotides that may, for example, be labeled byradioactivity, fluorescence or mass. The most common substitutions areat the 2′ position of the ribose sugar, where moieties such as H(hydrogen) F, NH₃, OCH₃ and other O— alkyl, alkenyl, alkynyl, andorthoesters, may be substituted, or in the phosphorous backbone, wheresulfur, amines or hydrocarbons may be substituted for the bridging ofnon-bridging atoms in the phosphodiester bond. Examples of modifiedsiRNAs are explained more fully in commonly assigned U.S. patentapplication Ser. No. 10/613,077, filed Jul. 1, 2003.

Additionally, as noted above, the cell type into which the siRNA isintroduced may affect the ability of the siRNA to enter the cell;however, it does not appear to affect the ability of the siRNA tofunction once it enters the cell. Methods for introducingdouble-stranded RNA into various cell types are well known to personsskilled in the art.

As persons skilled in the art are aware, in certain species, thepresence of proteins such as RdRP, the RNA-dependent RNA polymerase, maycatalytically enhance the activity of the siRNA. For example, RdRPpropagates the RNAi effect in C. elegans and other non-mammalianorganisms. In fact, in organisms that contain these proteins, the siRNAmay be inherited. Two other proteins that are well studied and known tobe a part of the machinery are members of the Argonaute family andDicer, as well as their homologues. There is also initial evidence thatthe RISC complex might be associated with the ribosome so the moreefficiently translated mRNAs will be more susceptible to silencing thanothers.

Another very important factor in the efficacy of siRNA is mRNAlocalization. In general, only cytoplasmic mRNAs are considered to beaccessible to RNAi to any appreciable degree. However, appropriatelydesigned siRNAs, for example, siRNAs modified with internucleotidelinkages or 2′-O-methyl groups, may be able to cause silencing by actingin the nucleus. Examples of these types of modifications are describedin commonly assigned U.S. patent application Ser. Nos. 10/431,027 and10/613,077.

As described above, even when one selects at least two siRNAs at random,the effectiveness of the two may be greater than one would predict basedon the effectiveness of two individual siRNAs. This additive orsynergistic effect is particularly noticeable as one increases to atleast three siRNAs, and even more noticeable as one moves to at leastfour siRNAs. Surprisingly, the pooling of the non-functional andsemi-functional siRNAs, particularly more than five siRNAs, can lead toa silencing mixture that is as effective if not more effective than anyone particular functional siRNA.

Within the kits of the present invention, preferably each siRNA will bepresent in a concentration of between 0.001 and 200 μM, more preferablybetween 0.01 and 200 nM, and most preferably between 0.1 and 10 nM.

In addition to preferably comprising at least four or five siRNAs, thekits of the present invention will also preferably comprise a buffer tokeep the siRNA duplex stable. Persons skilled in the art are aware ofbuffers suitable for keeping siRNA stable. For example, the buffer maybe comprised of 100 mM KCl, 30 mM HEPES-pH 7.5, and 1 mM MgCl₂.Alternatively, kits might contain complementary strands that contain anyone of a number of chemical modifications (e.g., a 2′-O-ACE) thatprotect the agents from degradation by nucleases. In this instance, theuser may (or may not) remove the modifying protective group (e.g.,deprotect) before annealing the two complementary strands together.

By way of example, the kits may be organized such that pools of siRNAduplexes are provided on an array or microarray of wells or drops for aparticular gene set or for unrelated genes. The array may, for example,be in 96 wells, 384 wells or 1284 wells arrayed in a plastic plate or ona glass slide using techniques now known or that come to be known topersons skilled in the art. Within an array, preferably there will becontrols such as functional anti-lamin A/C, cyclophilin and two siRNAduplexes that are not specific to the gene of interest.

In order to ensure stability of the siRNA pools prior to usage, they maybe retained in lyophilized form at minus twenty degrees (−20° C.) untilthey are ready for use. Prior to usage, they should be resuspended;however, even once resuspended, for example, in the aforementionedbuffer, they should be kept at minus twenty degrees, (−20° C.) untilused. The aforementioned buffer, prior to use, may be stored atapproximately 4° C. or room temperature. Effective temperatures at whichto conduct transfections are well known to persons skilled in the artand include for example, room temperature.

The kits may be applied either in vivo or in vitro. Preferably, thesiRNA of the pools or kits is applied to a cell through transfection,employing standard transfection protocols. These methods are well knownto persons skilled in the art and include the use of lipid-basedcarriers, electroporation, cationic carriers, and microinjection.Further, one could apply the present invention by synthesizingequivalent DNA sequences (either as two separate, complementary strands,or as hairpin molecules) instead of siRNA sequences and introducing theminto cells through vectors. Once in the cells, the cloned DNA could betranscribed, thereby forcing the cells to generate the siRNA. Examplesof vectors suitable for use with the present application include but arenot limited to the standard transient expression vectors, adenoviruses,retroviruses, lentivirus-based vectors, as well as other traditionalexpression vectors. Any vector that has an adequate siRNA expression andprocession module may be used. Furthermore, certain chemicalmodifications to siRNAs, including but not limited to conjugations toother molecules, may be used to facilitate delivery. For certainapplications it may be preferable to deliver molecules withouttransfection by simply formulating in a physiological acceptablesolution.

This embodiment may be used in connection with any of the aforementionedembodiments. Accordingly, the sequences within any pool may be selectedby rational design.

Multigene Silencing

In addition to developing kits that contain multiple siRNA directedagainst a single gene, another embodiment includes the use of multiplesiRNA targeting multiple genes. Multiple genes may be targeted throughthe use of high- or hyper-functional siRNA. High- or hyper-functionalsiRNA that exhibit increased potency, require lower concentrations toinduce desired phenotypic (and thus therapeutic) effects. Thiscircumvents RISC saturation. It therefore reasons that if lowerconcentrations of a single siRNA are needed for knockout or knockdownexpression of one gene, then the remaining (uncomplexed) RISC will befree and available to interact with siRNA directed against two, three,four, or more, genes. Thus in this embodiment, the authors describe theuse of highly functional or hyper-functional siRNA to knock out threeseparate genes. More preferably, such reagents could be combined toknockout four distinct genes. Even more preferably, highly functional orhyperfunctional siRNA could be used to knock out five distinct genes.Most preferably, siRNA of this type could be used to knockout orknockdown the expression of six or more genes.

Hyperfunctional siRNA

The term hyperfunctional siRNA (hf-siRNA) describes a subset of thesiRNA population that induces RNAi in cells at low- or sub-nanomolarconcentrations for extended periods of time. These traits, heightenedpotency and extended longevity of the RNAi phenotype, are highlyattractive from a therapeutic standpoint. Agents having higher potencyrequire lesser amounts of the molecule to achieve the desiredphysiological response, thus reducing the probability of side effectsdue to “off-target” interference. In addition to the potentialtherapeutic benefits associated with hyperfunctional siRNA, hf-siRNA arealso desirable from an economic perspective. Hyperfunctional siRNA maycost less on a per-treatment basis, thus reducing overall expendituresto both the manufacturer and the consumer.

Identification of hyperfunctional siRNA involves multiple steps that aredesigned to examine an individual siRNA agent's concentration- and/orlongevity-profiles. In one non-limiting example, a population of siRNAdirected against a single gene are first analyzed using the previouslydescribed algorithm (Formula VIII). Individual siRNA are then introducedinto a test cell line and assessed for the ability to degrade the targetmRNA. It is important to note that when performing this step it is notnecessary to test all of the siRNA. Instead, it is sufficient to testonly those siRNA having the highest SMARTSCORES™, or siRNA ranking(i.e., SMARTSCORES™, or siRNA ranking >−10). Subsequently, the genesilencing data is plotted against the SMARTSCORES™, or siRNA rankings(see FIG. 9). siRNA that (1) induce a high degree of gene silencing(i.e., they induce greater than 80% gene knockdown) and (2) havesuperior SMARTSCORES™ (i.e., a SMARTSCORE™, or siRNA ranking, of >−10,suggesting a desirable average internal stability profile) are selectedfor further investigations designed to better understand the molecule'spotency and longevity. In one, non-limiting study dedicated tounderstanding a molecule's potency, an siRNA is introduced into one (ormore) cell types in increasingly diminishing concentrations (e.g., 3.0→0.3 nM). Subsequently, the level of gene silencing induced by eachconcentration is examined and siRNA that exhibit hyperfunctional potency(i.e., those that induce 80% silencing or greater at, e.g., picomolarconcentrations) are identified. In a second study, the longevityprofiles of siRNA having high (>−10) SMARTSCORES™, or siRNA rankings andgreater than 80% silencing are examined. In one non-limiting example ofhow this is achieved, siRNA are introduced into a test cell line and thelevels of RNAi are measured over an extended period of time (e.g.,24-168 hrs). siRNAs that exhibit strong RNA interference patterns(i.e., >80% interference) for periods of time greater than, e.g., 120hours, are thus identified. Studies similar to those described above canbe performed on any and all of the >106 siRNA included in this documentto further define the most functional molecule for any given gene.Molecules possessing one or both properties (extended longevity andheightened potency) are labeled “hyperfunctional siRNA,” and earmarkedas candidates for future therapeutic studies.

While the example(s) given above describe one means by whichhyperfunctional siRNA can be isolated, neither the assays themselves northe selection parameters used are rigid and can vary with each family ofsiRNA. Families of siRNA include siRNAs directed against a single gene,or directed against a related family of genes.

The highest quality siRNA achievable for any given gene may varyconsiderably. Thus, for example, in the case of one gene (gene X),rigorous studies such as those described above may enable theidentification of an siRNA that, at picomolar concentrations, induces99⁺% silencing for a period of 10 days. Yet identical studies of asecond gene (gene Y) may yield an siRNA that at high nanomolarconcentrations (e.g., 100 nM) induces only 75% silencing for a period of2 days. Both molecules represent the very optimum siRNA for theirrespective gene targets and therefore are designated “hyperfunctional.”Yet due to a variety of factors including but not limited to targetconcentration, siRNA stability, cell type, off-target interference, andothers, equivalent levels of potency and longevity are not achievable.Thus, for these reasons, the parameters described in the beforementioned assays can vary. While the initial screen selected siRNA thathad SMARTSCORES™ above −10 and a gene silencing capability of greaterthan 80%, selections that have stronger (or weaker) parameters can beimplemented. Similarly, in the subsequent studies designed to identifymolecules with high potency and longevity, the desired cutoff criteria(i.e., the lowest concentration that induces a desirable level ofinterference, or the longest period of time that interference can beobserved) can vary. The experimentation subsequent to application of therational criteria of this application is significantly reduced where oneis trying to obtain a suitable hyperfunctional siRNA for, for example,therapeutic use. When, for example, the additional experimentation ofthe type described herein is applied by one skilled in the art with thisdisclosure in hand, a hyperfunctional siRNA is readily identified.

The siRNA may be introduced into a cell by any method that is now knownor that comes to be known and that from reading this disclosure, personsskilled in the art would determine would be useful in connection withthe present invention in enabling siRNA to cross the cellular membrane.These methods include, but are not limited to, any manner oftransfection, such as, for example, transfection employing DEAE-Dextran,calcium phosphate, cationic lipids/liposomes, micelles, manipulation ofpressure, microinjection, electroporation, immunoporation, use ofvectors such as viruses, plasmids, cosmids, bacteriophages, cellfusions, and coupling of the polynucleotides to specific conjugates orligands such as antibodies, antigens, or receptors, passiveintroduction, adding moieties to the siRNA that facilitate its uptake,and the like.

Having described the invention with a degree of particularity, exampleswill now be provided. These examples are not intended to and should notbe construed to limit the scope of the claims in any way.

EXAMPLES General Techniques and Nomenclatures

siRNA nomenclature. All siRNA duplexes are referred to by sense strand.The first nucleotide of the 5′-end of the sense strand is position 1,which corresponds to position 19 of the antisense strand for a 19-mer.In most cases, to compare results from different experiments, silencingwas determined by measuring specific transcript mRNA levels or enzymaticactivity associated with specific transcript levels, 24 hourspost-transfection, with siRNA concentrations held constant at 100 nM.For all experiments, unless otherwise specified, transfection efficiencywas ensured to be over 95%, and no detectable cellular toxicity wasobserved. The following system of nomenclature was used to compare andreport siRNA-silencing functionality: “F” followed by the degree ofminimal knockdown. For example, F50 signifies at least 50% knockdown,F80 means at least 80%, and so forth. For this study, all sub-F50 siRNAswere considered non-functional.

Cell culture and transfection. 96-well plates are coated with 50 μl of50 mg/ml poly-L-lysine (Sigma) for 1 hr, and then washed 3× withdistilled water before being dried for 20 min. HEK293 cells orHEK293Lucs or any other cell type of interest are released from theirsolid support by trypsinization, diluted to 3.5×10⁵ cells/ml, followedby the addition of 100 μL of cells/well. Plates are then incubatedovernight at 37° C., 5% CO₂. Transfection procedures can vary widelydepending on the cell type and transfection reagents. In onenon-limiting example, a transfection mixture consisting of 2 mL Opti-MEMI (Gibco-BRL), 80 μl Lipofectamine 2000 (Invitrogen), 15 μL SUPERNasinat 20 U/μl (Ambion), and 1.5 μl of reporter gene plasmid at 1 μg/μl isprepared in 5-ml polystyrene round bottom tubes. One hundred μl oftransfection reagent is then combined with 100 μl of siRNAs inpolystyrene deep-well titer plates (Beckman) and incubated for 20 to 30min at room temperature. Five hundred and fifty microliters of Opti-MEMis then added to each well to bring the final siRNA concentration to 100nM. Plates are then sealed with parafilm and mixed. Media is removedfrom HEK293 cells and replaced with 95 μl of transfection mixture. Cellsare incubated overnight at 37° C., 5% CO₂.

Quantification of gene knockdown. A variety of quantification procedurescan be used to measure the level of silencing induced by siRNA or siRNApools. In one non-limiting example: to measure mRNA levels 24 hrspost-transfection, QuantiGene branched-DNA (bDNA) kits (Bayer) (Wang, etal, Regulation of insulin preRNA splicing by glucose. Proc. Natl. Acad.Sci. USA 1997, 94:4360.) are used according to manufacturerinstructions. To measure luciferase activity, media is removed fromHEK293 cells 24 hrs post-transfection, and 50 μl of Steady-GLO reagent(Promega) is added. After 5 minutes, plates are analyzed on a platereader.

Example I Sequences Used to Develop the Algorithm

Anti-Firefly and anti-Cyclophilin siRNAs panels (FIG. 5 a, b) sortedaccording to using Formula VIII predicted values. All siRNAs scoringmore than 0 (formula VIII) and more then 20 (formula IX) are fullyfunctional. All ninety sequences for each gene (and DBI) appear below inTable III.

TABLE III Cyclo 1 SEQ. ID 0032 GUUCCAAAAACAGUGGAUA Cyclo 2 SEQ. ID 0033UCCAAAAACAGUGGAUAAU Cyclo 3 SEQ. ID 0034 CAAAAACAGUGGAUAAUUU Cyclo 4SEQ. ID 0035 AAAACACUGGAUAAUUUUG Cyclo 5 SEQ. ID 0036AACAGUGGAUAAUUUUGUG Cyclo 6 SEQ. ID 0037 CAGUGGAUAAUUUUGUGGC Cyclo 7SEQ. ID 0038 GUGGAUAAUUUUGUGGCCU Cyclo 8 SEQ. ID 0039GGAUAAUUUUGUGGCCUUA Cyclo 9 SEQ. ID 0040 AUAAUUUUGUGGCCUUAGC Cyclo 10SEQ. ID 0041 AAUUUUGUGGCCUUAGCUA Cyclo 11 SEQ. ID 0042UUUUGUGGCCUUAGCUACA Cyclo 12 SEQ. ID 0043 UUGUGGCCUUAGCUACAGG Cyclo 13SEQ. ID 0044 GUGGCCUUAGCUACAGGAG Cyclo 14 SEQ. ID 0045GGCCUUAGCUACAGGAGAG Cyclo 15 SEQ. ID 0046 CCUUAGCUACAGGAGAGAA Cyclo 16SEQ. ID 0047 UUAGCUACAGGAGAGAAAG Cyclo 17 SEQ. ID 0048AGCUACAGGAGAGAAAGGA Cyclo 18 SEQ. ID 0049 CUACAGGAGAGAAAGGAUU Cyclo 19SEQ. ID 0050 ACAGGAGAGAAAGGAUUUG Cyclo 20 SEQ. ID 0051AGGAGAGAAAGGAUUUGGC Cyclo 21 SEQ. ID 0052 GAGAGAAAGGAUUUGGCUA Cyclo 22SEQ. ID 0053 GAGAAAGGAUUUGGCUACA Cyclo 23 SEQ. ID 0054GAAAGGAUUUGGCUACAAA Cyclo 24 SEQ. ID 0055 AAGGAUUUGGCUACAAAAA Cyclo 25SEQ. ID 0056 GGAUUUGGCUACAAAAACA Cyclo 26 SEQ. ID 0057AUUUGGCUACAAAAACAGC Cyclo 27 SEQ. ID 0058 UUGGCUACAAAAACAGCAA Cyclo 28SEQ. ID 0059 GGCUACAAAAACAGCAAAU Cyclo 29 SEQ. ID 0060CUACAAAAACAGCAAAUUC Cyclo 30 SEQ. ID 0061 ACAAAAACAGCAAAUUCCA Cyclo 31SEQ. ID 0062 AAAAACAGCAAAUUCCAUC Cyclo 32 SEQ. ID 0063AAACAGCAAAUUCCAUCGU Cyclo 33 SEQ. ID 0064 ACAGCAAAUUCCAUCGUGU Cyclo 34SEQ. ID 0065 AGCAAAUUCCAUCGUGUAA Cyclo 35 SEQ. ID 0066CAAAUUCCAUCGUGUAAUC Cyclo 36 SEQ. ID 0067 AAUUCCAUCGUGUAAUCAA Cyclo 37SEQ. ID 0068 UUCCAUCGUGUAAUCAAGG Cyclo 38 SEQ. ID 0069CCAUCGUGUAAUCAAGGAC Cyclo 39 SEQ. ID 0070 AUCGUGUAAUCAAGGACUU Cyclo 40SEQ. ID 0071 CGUGUAAUCAAGGACUUCA Cyclo 41 SEQ. ID 0072UGUAAUCAAGGACUUCAUG Cyclo 42 SEQ. ID 0073 UAAUCAAGGACUUCAUGAU Cyclo 43SEQ. ID 0074 AUCAAGGACUUCAUGAUCC Cyclo 44 SEQ. ID 0075CAAGGACUUCAUGAUCCAG Cyclo 45 SEQ. ID 0076 AGGACUUCAUGAUCCAGGG Cyclo 46SEQ. ID 0077 GACUUCAUGAUCCAGGGCG Cyclo 47 SEQ. ID 0078CUUCAUGAUCCAGGGCGGA Cyclo 48 SEQ. ID 0079 UCAUGAUCCAGGGCGGAGA Cyclo 49SEQ. ID 0080 AUGAUCCAGGGCGGAGACU Cyclo 50 SEQ. ID 0081GAUCCAGGGCGGAGACUUC Cyclo 51 SEQ. ID 0082 UCCAGGGCGGAGACUUCAC Cyclo 52SEQ. ID 0083 CAGGGCGGAGACUUCACCA Cyclo 53 SEQ. ID 0084GGGCGGAGACUUCACCAGG Cyclo 54 SEQ. ID 0085 GCGGAGACUUCACCAGGGG Cyclo 55SEQ. ID 0086 GGAGACUUCACCAGGGGAG Cyclo 56 SEQ. ID 0087AGACUUCACCAGGGGAGAU Cyclo 57 SEQ. ID 0088 ACUUCACCAGGGGAGAUGG Cyclo 58SEQ. ID 0089 UUCACCAGGGGAGAUGGCA Cyclo 59 SEQ. ID 0090CACCAGGGGAGAUGGCACA Cyclo 60 SEQ. ID 0091 CCAGGGGAGAUGGCACAGG Cyclo 61SEQ. ID 0092 AGGGGAGAUGGCACAGGAG Cyclo 62 SEQ. ID 0093GGGAGAUGGCACAGGAGGA Cyclo 63 SEQ. ID 0094 GAGAUGGCACAGGAGGAAA Cyclo 64SEQ. ID 0095 GAUGGCACAGGAGGAAAGA Cyclo 65 SEQ. ID 0096UGGCACAGGAGGAAAGAGC Cyclo 66 SEQ. ID 0097 GCACAGGAGGAAAGAGCAU Cyclo 67SEQ. ID 0098 ACAGGAGGAAAGAGCAUCU Cyclo 68 SEQ. ID 0099AGGAGGAAAGAGCAUCUAC Cyclo 69 SEQ. ID 0100 GAGGAAAGAGCAUCUACGG Cyclo 70SEQ. ID 0101 GGAAAGAGCAUCUACGGUG Cyclo 71 SEQ. ID 0102AAAGAGCAUCUACGGUGAG Cyclo 72 SEQ. ID 0103 AGAGCAUCUACGGUGAGCG Cyclo 73SEQ. ID 0104 AGCAUCUACGGUGAGCGCU Cyclo 74 SEQ. ID 0105CAUCUACGGUGAGCGCUUC Cyclo 75 SEQ. ID 0106 UCUACGGUGAGCGCUUCCC Cyclo 76SEQ. ID 0107 UACGGUGAGCGCUUCCCCG Cyclo 77 SEQ. ID 0108CGGUCAGCGCUUCCCCGAU Cyclo 78 SEQ. ID 0109 GUGAGCGCUUCCCCGAUGA Cyclo 79SEQ. ID 0110 GAGCGCUUCCCCGAUGAGA Cyclo 80 SEQ. ID 0111GCGCUUCCCCGAUGAGAAC Cyclo 81 SEQ. ID 0112 GCUUCCCCGAUGAGAACUU Cyclo 82SEQ. ID 0113 UUCCCCGAUGAGAACUUCA Cyclo 83 SEQ. ID 0114CCCCGAUGAGAACUUCAAA Cyclo 84 SEQ. ID 0115 CCGAUGAGAACUUCAAACU Cyclo 85SEQ. ID 0116 GAUGAGAACUUCAAACUGA Cyclo 86 SEQ. ID 0117UGAGAACUUCAAACUGAAG Cyclo 87 SEQ. ID 0118 AGAACUUCAAACUGAAGCA Cyclo 88SEQ. ID 0119 AACUUCAAACUGAAGCACU Cyclo 89 SEQ. ID 0120CUUCAAACUGAAGCACUAC Cyclo 90 SEQ. ID 0121 UCAAACUGAAGCACUACGG DB 1 SEQ.ID 0122 ACGGGCAAGGCCAAGUGGG DB 2 SEQ. ID 0123 CGGGCAAGGCCAAGUGGGA DB 3SEQ. ID 0124 GGGCAAGGCCAAGUGGGAU DB 4 SEQ. ID 0125 GGCAAGGCCAAGUGGGAUGDB 5 SEQ. ID 0126 GCAAGGCCAAGUGGGAUGC DB 6 SEQ. ID 0127CAAGGCCAAGUGGGAUGCC DB 7 SEQ. ID 0128 AAGGCCAAGUGGGAUGCCU DB 8 SEQ. ID0129 AGGCCAAGUGGGAUGCCUG DB 9 SEQ. ID 0130 GGCCAAGUGGGAUGCCUGG DB 10SEQ. ID 0131 GCCAAGUGGGAUGCCUGGA DB 11 SEQ. ID 0132 CCAAGUGGGAUGCCUGGAADB 12 SEQ. ID 0133 CAAGUGGGAUGCCUGGAAU DB 13 SEQ. ID 0134AAGUGGGAUGCCUGGAAUG DB 14 SEQ. ID 0135 AGUGGGAUGCCUGGAAUGA DB 15 SEQ. ID0136 GUGGGAUGCCUGGAAUGAG DB 16 SEQ. ID 0137 UGGGAUGCCUGGAAUGAGC DB 17SEQ. ID 0138 GGGAUGCCUGGAAUGAGCU DB 18 SEQ. ID 0139 GGAUGCCUGGAAUGAGCUGDB 19 SEQ. ID 0140 GAUGCCUGGAAUGAGCUGA DB 20 SEQ. ID 0141AUGCCUGGAAUGAGCUGAA DB 21 SEQ. ID 0142 UGCCUGGAAUGAGCUGAAA DB 22 SEQ. ID0143 GCCUGGAAUGAGCUGAAAG DB 23 SEQ. ID 0144 CCUGGAAUGAGCUGAAAGG DB 24SEQ. ID 0145 CUGGAAUGAGCUGAAAGGG DB 25 SEQ. ID 0146 UGGAAUGAGCUGAAAGGGADB 26 SEQ. ID 0147 GGAAUGAGCUGAAAGGGAC DB 27 SEQ. ID 0148GAAUGAGCUGAAAGGGACU DB 28 SEQ. ID 0149 AAUGAGCUGAAAGGGACUU DB 29 SEQ. ID0150 AUGAGCUGAAAGGGACUUC DB 30 SEQ. ID 0151 UGAGCUGAAAGGGACUUCC DB 31SEQ. ID 0152 GAGCUGAAAGGGACUUCCA DB 32 SEQ. ID 0153 AGCUGAAAGGGACUUCCAADB 33 SEQ. ID 0154 GCUGAAAGGGACUUCCAAG DB 34 SEQ. ID 0155CUGAAAGGGACUUCCAAGG DB 35 SEQ. ID 0156 UGAAAGGGACUUCCAAGGA DB 36 SEQ. ID0157 GAAAGGGACUUCCAAGGAA DB 37 SEQ. ID 0158 AAAGGGACUUCCAAGGAAG DB 38SEQ. ID 0159 AAGGGACUUCCAAGGAAGA DB 39 SEQ. ID 0160 AGGGACUUCCAAGGAAGAUDB 40 SEQ. ID 0161 GGGACUUCCAAGGAAGAUG DB 41 SEQ. ID 0162GGACUUCCAAGGAAGAUGC DB 42 SEQ. ID 0163 GACUUCCAAGGAAGAUGCC DB 43 SEQ. ID0164 ACUUCCAAGGAAGAUGCCA DB 44 SEQ. ID 0165 CUUCCAAGGAAGAUGCCAU DB 45SEQ. ID 0166 UUCCAAGGAAGAUGCCAUG DB 46 SEQ. ID 0167 UCCAAGGAAGAUGCCAUGADB 47 SEQ. ID 0168 CCAAGGAAGAUGCCAUGAA DB 48 SEQ. ID 0169CAAGGAAGAUGCCAUGAAA DB 49 SEQ. ID 0170 AAGGAAGAUGCCAUGAAAG DB 50 SEQ. ID0171 AGGAAGAUGCCAUGAAAGC DB 51 SEQ. ID 0172 GGAAGAUGCCAUGAAAGCU DB 52SEQ. ID 0173 GAAGAUGCCAUGAAAGCUU DB 53 SEQ. ID 0174 AAGAUGCCAUGAAAGCUUADB 54 SEQ. ID 0175 AGAUGCCAUGAAAGCUUAC DB 55 SEQ. ID 0176GAUGCCAUGAAAGCUUACA DB 56 SEQ. ID 0177 AUGCCAUGAAAGCUUACAU DB 57 SEQ. ID0178 UGCCAUGAAAGCUUACAUC DB 58 SEQ. ID 0179 GCCAUGAAAGCUUACAUCA DB 59SEQ. ID 0180 CCAUGAAAGCUUACAUCAA DB 60 SEQ. ID 0181 CAUGAAAGCUUACAUCAACDB 61 SEQ. ID 0182 AUGAAAGCUUACAUCAACA DB 62 SEQ. ID 0183UGAAAGCUUACAUCAACAA DB 63 SEQ. ID 0184 GAAAGCUUACAUCAACAAA DB 64 SEQ. ID0185 AAAGCUUACAUCAACAAAG DB 65 SEQ. ID 0186 AAGCUUACAUCAACAAAGU DB 66SEQ. ID 0187 AGCUUACAUCAACAAAGUA DB 67 SEQ. ID 0188 GCUUACAUCAACAAAGUAGDB 68 SEQ. ID 0189 CUUACAUCAACAAAGUAGA DB 69 SEQ. ID 0190UUACAUCAACAAAGUAGAA DB 70 SEQ. ID 0191 UACAUCAACAAAGUAGAAG DB 71 SEQ. ID0192 ACAUCAACAAAGUAGAAGA DB 72 SEQ. ID 0193 CAUCAACAAAGUAGAAGAG DB 73SEQ. ID 0194 AUCAACAAAGUAGAAGAGC DB 74 SEQ. ID 0195 UCAACAAAGUAGAAGAGCUDB 75 SEQ. ID 0196 CAACAAAGUAGAAGAGCUA DB 76 SEQ. ID 0197AACAAAGUAGAAGAGCUAA DB 77 SEQ. ID 0198 ACAAAGUAGAAGAGCUAAA DB 78 SEQ. ID0199 CAAAGUAGAAGAGCUAAAG DB 79 SEQ. ID 0200 AAAGUAGAAGAGCUAAAGA DB 80SEQ. ID 0201 AAGUAGAAGAGCUAAAGAA DB 81 SEQ. ID 0202 AGUAGAAGAGCUAAAGAAADB 82 SEQ. ID 0203 GUAGAAGAGCUAAAGAAAA DB 83 SEQ. ID 0204UAGAAGAGCUAAAGAAAAA DB 84 SEQ. ID 0205 AGAAGAGCUAAAGAAAAAA DB 85 SEQ. ID0206 GAAGAGCUAAAGAAAAAAU DB 86 SEQ. ID 0207 AAGAGCUAAAGAAAAAAUA DB 87SEQ. ID 0208 AGAGCUAAAGAAAAAAUAC DB 88 SEQ. ID 0209 GAGCUAAAGAAAAAAUACGDB 89 SEQ. ID 0210 AGCUAAAGAAAAAAUACGG DB 90 SEQ. ID 0211GCUAAAGAAAAAAUACGGG Luc 1 SEQ. ID 0212 AUCCUCAUAAAGGCCAAGA Luc 2 SEQ. ID0213 AGAUCCUCAUAAAGGCCAA Luc 3 SEQ. ID 0214 AGAGAUCCUCAUAAAGGCC Luc 4SEQ. ID 0215 AGAGAGAUCCUCAUAAAGG Luc 5 SEQ. ID 0216 UCAGAGAGAUCCUCAUAAALuc 6 SEQ. ID 0217 AAUCAGAGAGAUCCUCAUA Luc 7 SEQ. ID 0218AAAAUCAGAGAGAUCCUCA Luc 8 SEQ. ID 0219 GAAAAAUCAGAGAGAUCCU Luc 9 SEQ. ID0220 AAGAAAAAUCAGAGAGAUC Luc 10 SEQ. ID 0221 GCAAGAAAAAUCAGAGAGA Luc 11SEQ. ID 0222 ACGCAAGAAAAAUCAGAGA Luc 12 SEQ. ID 0223 CGACGCAAGAAAAAUCAGALuc 13 SEQ. ID 0224 CUCGACGCAAGAAAAAUCA Luc 14 SEQ. ID 0225AACUCGACGCAAGAAAAAU Luc 15 SEQ. ID 0226 AAAACUCGACGCAAGAAAA Luc 16 SEQ.ID 0227 GGAAAACUCGACGCAAGAA Luc 17 SEQ. ID 0228 CCGGAAAACUCGACGCAAG Luc18 SEQ. ID 0229 UACCGGAAAACUCGACGCA Luc 19 SEQ. ID 0230CUUACCGGAAAACUCGACG Luc 20 SEQ. ID 0231 GUCUUACCGGAAAACUCGA Luc 21 SEQ.ID 0232 AGGUCUUACCGGAAAACUC Luc 22 SEQ. ID 0233 AAAGGUCUUACCGGAAAAC Luc23 SEQ. ID 0234 CGAAAGGUCUUACCGGAAA Luc 24 SEQ. ID 0235ACCGAAAGGUCUUACCGGA Luc 25 SEQ. ID 0236 GUACCGAAAGGUCUUACCG Luc 26 SEQ.ID 0237 AAGUACCGAAAGGUCUUAC Luc 27 SEQ. ID 0238 CGAAGUACCGAAAGGUCUU Luc28 SEQ. ID 0239 GACGAAGUACCGAAAGGUC Luc 29 SEQ. ID 0240UGGACGAAGUACCGAAAGG Luc 30 SEQ. ID 0241 UGUGGACGAAGUACCGAAA Luc 31 SEQ.ID 0242 UUUGUGGACGAAGUACCGA Luc 32 SEQ. ID 0243 UGUUUGUGGACGAAGUACC Luc33 SEQ. ID 0244 UGUGUUUGUGGACGAAGUA Luc 34 SEQ. ID 0245GUUGUGUUUGUGGACGAAG Luc 35 SEQ. ID 0246 GAGUUGUGUUUGUGGACGA Luc 36 SEQ.ID 0247 AGGAGUUGUGUUUGUGGAC Luc 37 SEQ. ID 0248 GGAGGAGUUGUGUUUGUGG Luc38 SEQ. ID 0249 GCGGAGGAGUUGUGUUUGU Luc 39 SEQ. ID 0250GCGCGGAGGAGUUGUGUUU Luc 40 SEQ. ID 0251 UUGCGCGGAGGAGUUGUGU Luc 41 SEQ.ID 0252 AGUUGCGCGGAGGAGUUGU Luc 42 SEQ. ID 0253 AAAGUUGCGCGGAGGAGUU Luc43 SEQ. ID 0254 AAAAAGUUGCGCGGAGGAG Luc 44 SEQ. ID 0255CGAAAAAGUUGCGCGGAGG Luc 45 SEQ. ID 0256 CGCGAAAAAGUUGCGCGGA Luc 46 SEQ.ID 0257 ACCGCGAAAAAGUUGCGCG Luc 47 SEQ. ID 0258 CAACCGCGAAAAAGUUGCG Luc48 SEQ. ID 0259 AACAACCGCGAAAAAGUUG Luc 49 SEQ. ID 0260GUAACAACCGCGAAAAAGU Luc 50 SEQ. ID 0261 AAGUAACAACCGCGAAAAA Luc 51 SEQ.ID 0262 UCAAGUAACAACCGCGAAA Luc 52 SEQ. ID 0263 AGUCAAGUAACAACCGCGA Luc53 SEQ. ID 0264 CCAGUCAAGUAACAACCGC Luc 54 SEQ. ID 0265CGCCAGUCAAGUAACAACC Luc 55 SEQ. ID 0266 GUCGCCAGUCAAGUAACAA Luc 56 SEQ.ID 0267 ACGUCGCCAGUCAAGUAAC Luc 57 SEQ. ID 0268 UUACGUCGCCAGUCAAGUA Luc58 SEQ. ID 0269 GAUUACGUCGCCAGUCAAG Luc 59 SEQ. ID 0270UGGAUUACGUCGCCAGUCA Luc 60 SEQ. ID 0271 CGUGGAUUACGUCGCCAGU Luc 61 SEQ.ID 0272 AUCGUGGAUUACGUCGCCA Luc 62 SEQ. ID 0273 AGAUCGUGGAUUACGUCGC Luc63 SEQ. ID 0274 AGAGAUCGUGGAUUACGUC Luc 64 SEQ. ID 0275AAAGAGAUCGUGGAUUACG Luc 65 SEQ. ID 0276 AAAAAGAGAUCGUGGAUUA Luc 66 SEQ.ID 0277 GGAAAAAGAGAUCGUGGAU Luc 67 SEQ. ID 0278 ACGGAAAAAGAGAUCGUGG Luc68 SEQ. ID 0279 UGACGGAAAAAGAGAUCGU Luc 69 SEQ. ID 0280GAUGACGGAAAAAGAGAUC Luc 70 SEQ. ID 0281 ACGAUGACGGAAAAAGAGA Luc 71 SEQ.ID 0282 AGACGAUGACGGAAAAAGA Luc 72 SEQ. ID 0283 AAAGACGAUGACGGAAAAA Luc73 SEQ. ID 0284 GGAAAGACGAUGACGGAAA Luc 74 SEQ. ID 0285ACGGAAAGACGAUGACGGA Luc 75 SEQ. ID 0286 GCACGGAAAGACGAUGACG Luc 76 SEQ.ID 0287 GAGCACGGAAAGACGAUGA Luc 77 SEQ. ID 0288 UGGAGCACGGAAAGACGAU Luc78 SEQ. ID 0289 UUUGGAGCACGGAAAGACG Luc 79 SEQ. ID 0290GUUUUGGAGCACGGAAAGA Luc 80 SEQ. ID 0291 UUGUUUUGGAGCACGGAAA Luc 81 SEQ.ID 0292 UGUUGUUUUGGAGCACGGA Luc 82 SEQ. ID 0293 GUUGUUGUUUUGGAGCACG Luc83 SEQ. ID 0294 CCGUUGUUGUUUUGGAGCA Luc 84 SEQ. ID 0295CGCCGUUGUUGUUUUGGAG Luc 85 SEQ. ID 0296 GCCGCCGUUGUUGUUUUGG Luc 86 SEQ.ID 0297 CCGCCGCCGUUGUUGUUUU Luc 87 SEQ. ID 0298 UCCCGCCGCCGUUGUUGUU Luc88 SEQ. ID 0299 CUUCCCGCCGCCGUUGUUG Luc 89 SEQ. ID 0300AACUUCCCGCCGCCGUUGU Luc 90 SEQ. ID 0301 UGAACUUCCCGCCGCCGUU

Example II Validation of the Algorithm Using DBI, Luciferase, PLK, EGFR,and SEAP

The algorithm (Formula VIII) identified siRNAs for five genes, humanDBI, firefly luciferase (fLuc), renilla luciferase (rLuc), human PLK,and human secreted alkaline phosphatase (SEAP). Four individual siRNAswere selected on the basis of their SMARTSCORES™ derived by analysis oftheir sequence using Formula VIII (all of the siRNAs would be selectedwith Formula IX as well) and analyzed for their ability to silence theirtargets' expression. In addition to the scoring, a BLAST search wasconducted for each siRNA. To minimize the potential for off-targetsilencing effects, only those target sequences with more than threemismatches against un-related sequences were selected. Semizarov, et al.(2003) Specificity of short interfering RNA determined through geneexpression signatures, Proc. Natl. Acad. Sci. USA, 100:6347. Theseduplexes were analyzed individually and in pools of 4 and compared withseveral siRNAs that were randomly selected. The functionality wasmeasured as a percentage of targeted gene knockdown as compared tocontrols. All siRNAs were transfected as described by the methods aboveat 100 nM concentration into HEK293 using Lipofectamine 2000. The levelof the targeted gene expression was evaluated by B-DNA as describedabove and normalized to the non-specific control. FIG. 10 shows that thesiRNAs selected by the algorithm disclosed herein were significantlymore potent than randomly selected siRNAs. The algorithm increased thechances of identifying an F50 siRNA from 48% to 91%, and an F80 siRNAfrom 13% to 57%. In addition, pools of SMART siRNA silence the selectedtarget better than randomly selected pools (see FIG. 10F).

Example III Validation of the Algorithm Using Genes Involved inClathrin-Dependent Endocytosis

Components of clathrin-mediated endocytosis pathway are key tomodulating intracellular signaling and play important roles in disease.Chromosomal rearrangements that result in fusion transcripts between theMixed-Lineage Leukemia gene (MLL) and CALM (clathrin assembly lymphoidmyeloid leukemia gene) are believed to play a role in leukemogenesis.Similarly, disruptions in Rab7 and Rab9, as well as HIP1(Huntingtin-interacting protein), genes that are believed to be involvedin endocytosis, are potentially responsible for ailments resulting inlipid storage, and neuronal diseases, respectively. For these reasons,siRNA directed against clathrin and other genes involved in theclathrin-mediated endocytotic pathway are potentially important researchand therapeutic tools.

siRNAs directed against genes involved in the clathrin-mediatedendocytosis pathways were selected using Formula VIII. The targetedgenes were clathrin heavy chain (CHC, accession # NM_(—)004859),clathrin light chain A (CLCa, NM_(—)001833), clathrin light chain B(CLCb, NM_(—)001834), CALM (U45976), β2 subunit of AP-2 (β2,NM_(—)001282), Eps15 (NM_(—)001981), Eps15R(NM_(—)021235), dynamin II(DYNII, NM_(—)004945), Rab5a (BC001267), Rab5b (NM_(—)002868), Rab5c(AF141304), and EEA.1 (XM_(—)018197).

For each gene, four siRNAs duplexes with the highest scores wereselected and a BLAST search was conducted for each of them using theHuman EST database. In order to minimize the potential for off-targetsilencing effects, only those sequences with more than three mismatchesagainst un-related sequences were used. All duplexes were synthesized atDharmacon, Inc. as 21-mers with 3′-UU overhangs using a modified methodof 2′-ACE chemistry, Scaringe (2000) Advanced 5′-silyl-2′-orthoesterapproach to RNA oligonucleotide synthesis, Methods Enzymol. 317:3, andthe antisense strand was chemically phosphorylated to insure maximizedactivity.

HeLa cells were grown in Dulbecco's modified Eagle's medium (DMEM)containing 10% fetal bovine serum, antibiotics and glutamine. siRNAduplexes were resuspended in 1×siRNA Universal buffer (Dharmacon, Inc.)to 20 μM prior to transfection. HeLa cells in 12-well plates weretransfected twice with 4 μl of 20 μM siRNA duplex in 3 μl Lipofectamine2000 reagent (Invitrogen, Carlsbad, Calif., USA) at 24-hour intervals.For the transfections in which 2 or 3 siRNA duplexes were included, theamount of each duplex was decreased, so that the total amount was thesame as in transfections with single siRNAs. Cells were plated intonormal culture medium 12 hours prior to experiments, and protein levelswere measured 2 or 4 days after the first transfection.

Equal amounts of lysates were resolved by electrophoresis, blotted, andstained with the antibody specific to targeted protein, as well asantibodies specific to unrelated proteins, PP1 phosphatase and Tsg101(not shown). The cells were lysed in Triton X-100/glycerolsolubilization buffer as described previously. Tebar, Bohlander, &Sorkin (1999) Clathrin Assembly Lymphoid Myeloid Leukemia (CALM)Protein: Localization in Endocytic-coated Pits, Interactions withClathrin, and the Impact of Overexpression on Clathrin-mediated Traffic,Mol. Biol. Cell, 10:2687. Cell lysates were electrophoresed, transferredto nitrocellulose membranes, and Western blotting was performed withseveral antibodies followed by detection using enhancedchemiluminescence system (Pierce, Inc). Several x-ray films wereanalyzed to determine the linear range of the chemiluminescence signals,and the quantifications were performed using densitometry andAlphaImager v5.5 software (Alpha Innotech Corporation). In experimentswith Eps15R-targeted siRNAs, cell lysates were subjected toimmunoprecipitation with Ab860, and Eps15R was detected inimmunoprecipitates by Western blotting as described above.

The antibodies to assess the levels of each protein by Western blot wereobtained from the following sources: monoclonal antibody to clathrinheavy chain (TD.1) was obtained from American Type Culture Collection(Rockville, Md., USA); polyclonal antibody to dynamin II was obtainedfrom Affinity Bioreagents, Inc. (Golden, Colo., USA); monoclonalantibodies to EEA. 1 and Rab5a were purchased from BD TransductionLaboratories (Los Angeles, Calif., USA); the monoclonal antibody toTsg101 was purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz,Calif., USA); the monoclonal antibody to GFP was from ZYMED LaboratoriesInc. (South San Francisco, Calif., USA); the rabbit polyclonalantibodies Ab32 specific to α-adaptins and Ab20 to CALM were describedpreviously (Sorkin et al. (1995) Stoichiometric Interaction of theEpidermal Growth Factor Receptor with the Clathrin-associated ProteinComplex AP-2, J. Biol. Chem., 270:619), the polyclonal antibodies toclathrin light chains A and B were kindly provided by Dr. F. Brodsky(UCSF); monoclonal antibodies to PP1 (BD Transduction Laboratories) andα-Actinin (Chemicon) were kindly provided by Dr. M. Dell'Acqua(University of Colorado); Eps15 Ab577 and Eps15R Ab860 were kindlyprovided by Dr. P.P. Di Fiore (European Cancer Institute).

FIG. 11 demonstrates the in vivo functionality of 48 individual siRNAs,selected using Formula VIII (most of them will meet the criteriaincorporated by Formula IX as well) targeting 12 genes. Various celllines were transfected with siRNA duplexes (Dupl-4) or pools of siRNAduplexes (Pool), and the cells were lysed 3 days after transfection withthe exception of CALM (2 days) and β2 (4 days).

Note a β1′-adaptin band (part of AP-1 Golgi adaptor complex) that runsslightly slower than β2 adaptin. CALM has two splice variants, 66 and 72kD. The full-length Eps15R (a doublet of ˜130 kD) and several truncatedspliced forms of ˜100 kD and ˜70 kD were detected in Eps15Rimmunoprecipitates (shown by arrows). The cells were lysed 3 days aftertransfection. Equal amounts of lysates were resolved by electrophoresisand blotted with the antibody specific to a targeted protein (GFPantibody for YFP fusion proteins) and the antibody specific to unrelatedproteins PP1 phosphatase or α-actinin, and TSG101. The amount of proteinin each specific band was normalized to the amount of non-specificproteins in each lane of the gel. Nearly all of them appear to befunctional, which establishes that Formula VIII and IX can be used topredict siRNAs' functionality in general in a genome wide manner.

To generate the fusion of yellow fluorescent protein (YFP) with Rab5b orRab5c (YFP-Rab5b or YFP-Rab5c), a DNA fragment encoding the full-lengthhuman Rab5b or Rab5c was obtained by PCR using Pfu polymerase(Stratagene) with a SacI restriction site introduced into the 5′ end anda KpnI site into the 3′ end and cloned into pEYFP-C1 vector (CLONTECH,Palo Alto, Calif., USA). GFP-CALM and YFP-Rab5a were describedpreviously (Tebar, Bohlander, & Sorkin (1999) Clathrin Assembly LymphoidMyeloid Leukemia (CALM) Protein: Localization in Endocytic-coated Pits,Interactions with Clathrin, and the Impact of Overexpression onClathrin-mediated Traffic, Mol. Biol. Cell 10:2687).

Example IV Validation of the Algorithm Using EG5, GADPH, ATE1, MEK2,MEK1, QB, Lamina/C, C-myc, Human Cyclophilin, and Mouse Cyclophilin

A number of genes have been identified as playing potentially importantroles in disease etiology. Expression profiles of normal and diseasedkidneys has implicated Edg5 in immunoglobulin A neuropathy, a commonrenal glomerular disease. Myc1, MEK1/2 and other related kinases havebeen associated with one or more cancers, while lamins have beenimplicated in muscular dystrophy and other diseases. For these reasons,siRNA directed against the genes encoding these classes of moleculeswould be important research and therapeutic tools.

FIG. 12 illustrates four siRNAs targeting 10 different genes (Table Vfor sequence and accession number information) that were selectedaccording to the Formula VIII and assayed as individuals and pools inHEK293 cells. The level of siRNA induced silencing was measured usingthe B-DNA assay. These studies demonstrated that thirty-six out of theforty individual SMART-selected siRNA tested are functional (90%) andall 10 pools are fully functional.

Example V Validation of the Algorithm Using BCL2

Bcl-2 is a ˜25 kD, 205-239 amino acid, anti-apoptotic protein thatcontains considerable homology with other members of the BCL familyincluding BCLX, MCL1, BAX, BAD, and BIK. The protein exists in at leasttwo forms (Bcl2a, which has a hydrophobic tail for membrane anchorage,and Bcl2b, which lacks the hydrophobic tail) and is predominantlylocalized to the mitochondrial membrane. While Bcl2 expression is widelydistributed, particular interest has focused on the expression of thismolecule in B and T cells. Bcl2 expression is down-regulated in normalgerminal center B cells yet in a high percentage of follicularlymphomas, Bcl2 expression has been observed to be elevated. Cytologicalstudies have identified a common translocation ((14; 18)(q32;q32))amongst a high percentage (>70%) of these lymphomas. This genetic lesionplaces the Bcl2 gene in juxtaposition to immunoglobulin heavy chain gene(IgH) encoding sequences and is believed to enforce inappropriate levelsof gene expression, and resistance to programmed cell death in thefollicle center B cells. In other cases, hypomethylation of the Bcl2promoter leads to enhanced expression and again, inhibition ofapoptosis. In addition to cancer, dysregulated expression of Bcl-2 hasbeen correlated with multiple sclerosis and various neurologicaldiseases.

The correlation between Bcl-2 translocation and cancer makes this genean attractive target for RNAi. Identification of siRNA directed againstthe bcl2 transcript (or Bcl2-IgH fusions) would further ourunderstanding Bcl2 gene function and possibly provide a futuretherapeutic agent to battle diseases that result from altered expressionor function of this gene.

In Silico Identification of Functional siRNA

To identify functional and hyperfunctional siRNA against the Bcl2 gene,the sequence for Bcl-2 was downloaded from the NCBI Unigene database andanalyzed using the Formula VIII algorithm. As a result of theseprocedures, both the sequence and SMARTSCORES™, or siRNA rankings of theBcl2 siRNA were obtained and ranked according to their functionality.Subsequently, these sequences were BLAST'ed (database) to insure thatthe selected sequences were specific and contained minimal overlap withunrealated genes. The SMARTSCORES™, or siRNA rankings for the top 10Bcl-2 siRNA are identified in FIG. 13.

In Vivo Testing of Bcl-2 SiRNA

Bcl-2 siRNAs having the top ten SMARTSCORES™, or siRNA rankings wereselected and tested in a functional assay to determine silencingefficiency. To accomplish this, each of the ten duplexes weresynthesized using 2′-O-ACE chemistry and transfected at 100 nMconcentrations into cells. Twenty-four hours later assays were performedon cell extracts to assess the degree of target silencing. Controls usedin these experiments included mock transfected cells, and cells thatwere transfected with a non-specific siRNA duplex.

The results of these experiments are presented below (and in FIG. 14)and show that all ten of the selected siRNA induce 80% or bettersilencing of the Bcl2 message at 100 nM concentrations. These dataverify that the algorithm successfully identified functional Bcl2 siRNAand provide a set of functional agents that can be used in experimentaland therapeutic environments.

siRNA 1 GGGAGAUAGUGAUGAAGUA SEQ. ID NO. 302 siRNA 2 GAAGUACAUCCAUUAUAAGSEQ. ID NO. 303 siRNA 3 GUACGACAACCGGGAGAUA SEQ. ID NO. 304 siRNA 4AGAUAGUGAUGAAGUACAU SEQ. ID NO. 305 siRNA 5 UGAAGACUCUGCUCAGUUU SEQ. IDNO. 306 siRNA 6 GCAUGCGGCCUCUGUUUGA SEQ. ID NO. 307 siRNA 7UGCGGCCUCUGUUUGAUUU SEQ. ID NO. 308 siRNA 8 GAGAUAGUGAUGAAGUACA SEQ. IDNO. 309 siRNA 9 GGAGAUAGUGAUGAAGUAC SEQ. ID NO. 310 siRNA 10GAAGACUCUGCUCAGUUUG SEQ. ID NO. 311 Bcl2 siRNA: Sense Strand, 5′→3′

Example VI Sequences Selected by the Algorithm

Sequences of the siRNAs selected using Formulas (Algorithms) VIII and IXwith their corresponding ranking, which have been evaluated for thesilencing activity in vivo in the present study (Formula VIII and IX,respectively) are shown in Table V. It should be noted that the “t”residues in Table V, and elsewhere, when referring to siRNA, should bereplaced by “u” residues.

TABLE V SEQ. ID FORMULA FORMULA GENE Name No. FTLLSEQTENCE VIII IX CLTCNM_004859 0312 GAAAGAATCTGTAGAGAAA 76 94.2 CLTC NM_004859 0313GCAATGAGCTGTTTGAAGA 65 39.9 CLTC NM_004859 0314 TGACAAAGGTGGATAAATT 5738.2 CLTC NM_004859 0315 GGAAATGGATCTCTTTGAA 54 49.4 CLTA NM_001833 0316GGAAAGTAATGGTCCAACA 22 55.5 CLTA NM_001833 0317 AGACAGTTATGCAGCTATT 422.9 CLTA NM_001833 0318 CCAATTCTCGGAAGCAAGA 1 17 CLTA NM_001833 0319GAAAGTAATGGTCCAACAG −1 −13 CLTB NM_001834 0320 GCGCCAGAGTGAACAAGTA 1757.5 CLTB NM_001834 0321 GAAGGTGGCCCAGCTATGT 15 −8.6 CLTB NM_001834 0322GGAACCAGCGCCAGAGTGA 13 40.5 CLTB NM_001834 0323 GAGCGAGATTGCAGGCATA 2061.7 CALM U45976 0324 GTTAGTATCTGATGACTTG 36 −34.6 CALM U45976 0325GAAATGGAACCACTAAGAA 33 46.1 CALM U45976 0326 GGAAATGGAACCACTAAGA 30 61.2CALM U45976 0327 CAACTACACTTTCCAATGC 28 6.8 EPS15 NM_001981 0328CCACCAAGATTTCATGATA 48 25.2 EPS15 NM_001981 0329 GATCGGAACTCCAACAAGA 4349.3 EPS15 NM_001981 0330 AAACGGAGCTACAGATTAT 39 11.5 EPS15 NM_0019810331 CCACACAGCATTCTTGTAA 33 −23.6 EPS15R NM_021235 0332GAAGTTACCTTGAGCAATC 48 33 EPS15R NM_021235 0333 GGACTTGGCCGATCCAGAA 2733 EPS15R NM_021235 0334 GCACTTGGATCGAGATGAG 20 1.3 EPS15R NM_0212350335 CAAAGACCAATTCGCGTTA 17 27.7 DNM2 NM_004945 0336 CCGAATCAATCGCATCTTC6 −29.6 DNM2 NM_004945 0337 GACATGATCCTGCAGTTCA 5 −14 DNM2 NM_0049450338 GAGCGAATCGTCACCACTT 5 24 DNM2 NM_004945 0339 CCTCCGAGCTGGCGTCTAC −4−63.6 ARF6 AF93885 0340 TCACATGGTTAACCTCTAA 27 −21.1 ARF6 AF93885 0341GATGAGGGACGCCATAATC 7 −38.4 ARF6 AF93885 0342 CCTCTAACTACAAATCTTA 4 16.9ARF6 AF93885 0343 GGAAGGTGCTATCCAAAAT 4 11.5 RAB5A BC001267 0344GCAAGCAAGTCCTAACATT 40 25.1 RAB5A BC001267 0345 GGAAGAGGAGTAGACCTTA 1750.1 RAB5A BC001267 0346 AGGAATCAGTGTTGTAGTA 16 11.5 RAB5A BC001267 0347GAAGAGGAGTAGACCTTAC 12 7 RAB5B NM_002868 0348 GAAAGTCAAGCCTGGTATT 1418.1 RAB5B NM_002868 0349 AAAGTCAAGCCTGGTATTA 6 −17.8 RAB5B NM_0028680350 GCTATGAACGTGAATGATC 3 −21.1 RAB5B NM_002868 0351CAAGCCTGGTATTACGTTT −7 −37.5 RAB5C AF141304 0352 GGAACAAGATCTGTCAATT 3851.9 RAB5C AF141304 0353 GCAATGAACGTGAACGAAA 29 43.7 RAB5C AF141304 0354CAATGAACGTGAACGAAAT 18 43.3 RAB5C AF141304 0355 GGACAGGAGCGGTATCACA 618.2 EEA1 XM_018197 0356 AGACAGAGCTTGAGAATAA 67 64.1 EEA1 XM_018197 0357GAGAAGATCTTTATGCAAA 60 48.7 EEA1 XM_018197 0358 GAAGAGAAATCAGCAGATA 5845.7 EEA1 XM_018197 0359 GCAAGTAACTCAACTAACA 56 72.3 AP2B1 NM_0012820360 GAGCTAATCTGCCACATTG 49 −12.4 AP2B1 NM_001282 0361GCAGATGAGTTACTAGAAA 44 48.9 AP2B1 NM_001282 0362 CAACTTAATTGTCCAGAAA 4128.2 AP2B1 NM_001282 0363 CAACACAGGATTCTGATAA 33 −5.8 PLK NM_005030 0364AGATTGTGCCTAAGTCTCT −35 −3.4 PLK NM_005030 0365 ATGAAGATCTGGAGGTGAA 0−4.3 PLK NM_005030 0366 TTTGAGACTTCTTGCCTAA −5 −27.7 PLK NM_005030 0367AGATCACCCTCCTTAAATA 15 72.3 GAPDH NM_002046 0368 CAACGGATTTGGTCGTATT 27−2.8 GAPDH NM_002046 0369 GAAATCCCATCACCATCTT 24 3.9 GAPDH NM_0020460370 GACCTCAACTACATGGTTT 22 −22.9 GAPDH NM_002046 0371TGGTTTACATGTTCCAATA 9 9.8 c-Myc 0372 GAAGAAATCGATGTTGTTT 31 −11.7 c-Myc0373 ACACAAACTTGAACAGCTA 22 51.3 c-Myc 0374 GGAAGAAATCGATGTTGTT 18 26c-Myc 0375 GAAACGACGAGAACAGTTG 18 −8.9 MAP2K1 NM_002755 0376GCACATGGATGGAGGTTCT 26 16 MAP2K1 NM_002755 0377 GCAGAGAGAGCAGATTTGA 160.4 MAP2K1 NM_002755 0378 GAGGTTCTCTGGATCAAGT 14 15.5 MAP2K1 NM_0027550379 GAGCAGATTTGAAGCAACT 14 18.5 MAP2K2 NM_030662 0380CAAAGACGATGACTTCGAA 37 26.4 MAP2K2 NM_030662 0381 GATCAGCATTTGCATGGAA 24−0.7 MAP2K2 NM_030662 0382 TCCAGGAGTTTGTCAATAA 17 −4.5 MAP2K2 NM_0306620383 GGAAGCTGATCCACCTTGA 16 59.2 KNSL1(EG5) NM_004523 0384GCAGAAATCTAAGGATATA 53 35.8 KNSL1(EG5) NM_004523 0385CAACAAGGATGAAGTCTAT 50 18.3 KNSL1(EG5) NM_004523 0386CAGCAGAAATCTAAGGATA 41 32.7 KNSL1(EG5) NM_004523 0387CTAGATGGCTTTCTCAGTA 39 3.9 CyclophilinA NM_021130 0388AGACAAGGTCCCAAAGACA −16 58.1 CyclophilinA NM_021130 0389GGAATGGCAAGACCAGCAA −6 36 CyclophilinA NM_021130 0390AGAATTATTCCAGGGTTTA −3 16.1 CyclophilinA NM_021130 0391GCAGACAAGGTCCCAAAGA 8 8.9 LAMIN A/C NM_170707 0392 AGAAGCAGCTTCAGGATGA31 38.8 LAMIN A/C NM_170707 0393 GAGCTTGACTTCCAGAAGA 33 22.4 LAMIN A/CNM_170707 0394 CCACCGAAGTTCACCCTAA 21 27.5 LAMIN A/C NM_170707 0395GAGAAGAGCTCCTCCATCA 55 30.1 CyclophilinB M60857 0396 GAAAGAGCATCTACGGTGA41 83.9 CyclophilinB M60857 0397 GAAAGGATTTGGCTACAAA 53 59.1CyclophilinB M60857 0398 ACAGCAAATTCCATCGTGT −20 28.8 CyclophilinBM60857 0399 GGAAAGACTGTTCCAAAAA 2 27 DBI1 NM_020548 0400CAACACGCCTCATCCTCTA 27 −7.6 DBI2 NM_020548 0401 CATGAAAGCTTACATCAAC 25−30.8 DBI3 NM_020548 0402 AAGATGCCATGAAAGCTTA 17 22 DBI4 NM_020548 0403GCACATACCGCCTGAGTCT 15 3.9 rLUC1 0404 GATCAAATCTGAAGAAGGA 57 49.2 rLUC20405 GCCAAGAAGTTTCCTAATA 50 13.7 rLUC3 0406 CAGCATATCTTGAACCATT 41 −2.2rLUC4 0407 GAACAAAGGAAACGGATGA 39 29.2 SeAP1 NM_031313 0408CGGAAACGGTCCAGGCTAT 6 26.9 SeAP2 NM_031313 0409 GCTTCGAGCAGACATGATA 4−11.2 SeAP3 NM_031313 0410 CCTACACGGTCCTCCTATA 4 4.9 SeAP4 NM_0313130411 GCCAAGAACCTCATCATCT 1 −9.9 fLUC1 0412 GATATGGGCTGAATACAAA 54 40.4fLUC2 0413 GCACTCTGATTGACAAATA 47 54.7 fLUC3 0414 TGAAGTCTCTGATTAAGTA 4634.5 fLUC4 0415 TCAGAGAGATCCTCATAAA 40 11.4 mCyclo_1 NM_008907 0416GCAAGAAGATCACCATTTC 52 46.4 mCyclo_2 NM_008907 0417 GAGAGAAATTTGAGGATGA36 70.7 mCyclo_3 NM_008907 0418 GAAAGGATTTGGCTATAAG 35 −1.5 mCyclo_4NM_008907 0419 GAAAGAAGGCATGAACATT 27 10.3 BCL2_1 NM_000633 0420GGGAGATAGTGATGAAGTA 21 72 BCL2_2 NM_000633 0421 GAAGTACATCCATTATAAG 13.3 BCL2_3 NM_000633 0422 GTACGACAACCGGGAGATA 1 35.9 BCL2_4 NM_0006330423 AGATAGTGATGAAGTACAT −12 22.1 BCL2_5 NM_000633 0424TGAAGACTCTGCTCAGTTT 36 19.1 BCL2_6 NM_000633 0425 GCATGCGGCCTCTGTTTGA 5−9.7 QB1 NM_003365.1 0426 GCACACAGCUUACUACAUC 52 −4.8 QB2 NM_003365.10427 GAAAUGCCCUGGUAUCUCA 49 22.1 QB3 NM_003365.1 0426GAAGGAACGUGAUGUGAUC 34 22.9 QB4 NM_003365.1 0429 GCACUACUCCUGUGUGUGA 2820.4 ATE1-1 NM_007041 0430 GAACCCAGCUGGAGAACUU 45 15.5 ATE1-2 NM_0070410431 GAUAUACAGUGUGAUCUUA 40 12.2 ATE1-3 NM_007041 0432GUACUACGAUCCUGAUUAU 37 32.9 ATE1-4 NM_007041 0433 GUGCCGACCUUUACAAUUU 3518.2 EGFR-1 NM_005228 0434 GAAGGAAACTGAATTCAAA 68 79.4 EGFR-1 NM_0052280435 GGAAATATGTACTACGAAA 49 49.5 EGFR-1 NM_005228 0436CCACAAAGCAGTGAATTTA 41 7.6 EGFR-1 NM_005228 0437 GTAACAAGCTCACGCAGTT 4025.9

Many of the genes to which the described siRNA are directed playcritical roles in disease etiology. For this reason, the siRNAs listedin the sequence listing may potentially act as therapeutic agents. Anumber of prophetic examples follow and should be understood in view ofthe siRNA that are identified in the sequence listing. To isolate thesesiRNAs, the appropriate message sequence for each gene is analyzed usingone of the before mentioned formulas (preferably formula VIII) toidentify potential siRNA targets. Subsequently these targets areBLAST'ed to eliminate homology with potential off-targets.

Example VII Evidence for the Benefits of Pooling

Evidence for the benefits of pooling have been demonstrated using thereporter gene, luciferase. Ninety siRNA duplexes were synthesized usingDharmacon proprietary ACE® chemistry against one of the standardreporter genes: firefly luciferase. The duplexes were designed to starttwo base pairs apart and to cover approximately 180 base pairs of theluciferase gene (see sequences in Table III). Subsequently, the siRNAduplexes were co-transfected with a luciferase expression reporterplasmid into HEK293 cells using standard transfection protocols andluciferase activity was assayed at 24 and 48 hours.

Transfection of individual siRNAs showed standard distribution ofinhibitory effect. Some duplexes were active, while others were not.FIG. 15 represents a typical screen of ninety siRNA duplexes (SEQ. IDNO. 0032-0120) positioned two base pairs apart. As the figure suggests,the functionality of the siRNA duplex is determined more by a particularsequence of the oligonucleotide than by the relative oligonucleotideposition within a gene or excessively sensitive part of the mRNA, whichis important for traditional anti-sense technology.

When two continuous oligonucleotides were pooled together, a significantincrease in gene silencing activity was observed (see FIGS. 16A and B).A gradual increase in efficacy and the frequency of pools functionalitywas observed when the number of siRNAs increased to 3 and 4 (FIGS. 16A,16B, 17A, and 17B). Further, the relative positioning of theoligonucleotides within a pool did not determine whether a particularpool was functional (see FIGS. 18A and 18B, in which 100% of pools ofoligonucleotides distanced by 2, 10 and 20 base pairs were functional).

However, relative positioning may nonetheless have an impact. Anincreased functionality may exist when the siRNA are positionedcontinuously head to toe (5′ end of one directly adjacent to the 3′ endof the others).

Additionally, siRNA pools that were tested performed at least as well asthe best oligonucleotide in the pool, under the experimental conditionswhose results are depicted in FIG. 19. Moreover, when previouslyidentified non-functional and marginally (semi) functional siRNAduplexes were pooled together in groups of five at a time, a significantfunctional cooperative action was observed (see FIG. 20). In fact, poolsof semi-active oligonucleotides were 5 to 25 times more functional thanthe most potent oligonucleotide in the pool. Therefore, pooling severalsiRNA duplexes together does not interfere with the functionality of themost potent siRNAs within a pool, and pooling provides an unexpectedsignificant increase in overall functionality

Example VIII Additional Evidence of the Benefits of Pooling

Experiments were performed on the following genes: β-galactosidase,Renilla luciferase, and Secreted alkaline phosphatase, whichdemonstrates the benefits of pooling. (see FIGS. 21A, 21B and 21C).Individual and pools of siRNA (described in Figure legends 21A-C) weretransfected into cells and tested for silencing efficiency.Approximately 50% of individual siRNAs designed to silence theabove-specified genes were functional, while 100% of the pools thatcontain the same siRNA duplexes were functional.

Example IX Highly Functional siRNA

Pools of five siRNAs in which each two siRNAs overlap to 10-90% resultedin 98% functional entities (>80% silencing). Pools of siRNAs distributedthroughout the mRNA that were evenly spaced, covering an approximate20-2000 base pair range, were also functional. When the pools of siRNAwere positioned continuously head to tail relative to mRNA sequences andmimicked the natural products of Dicer cleaved long double stranded RNA,98% of the pools evidenced highly functional activity (>95% silencing).

Example X Human Cyclophilin B

Table III above lists the siRNA sequences for the human cyclophilin Bprotein. A particularly functional siRNA may be selected by applyingthese sequences to any of Formula I to VII above.

Alternatively, one could pool 2, 3, 4, 5 or more of these sequences tocreate a kit for silencing a gene. Preferably, within the kit therewould be at least one sequence that has a relatively high predictedfunctionality when any of Formulas I-VII is applied.

Example XI Sample Pools of siRNAs and their Application to Human Disease

The genetic basis behind human disease is well documented and siRNA maybe used as both research or diagnostic tools and therapeutic agents,either individually or in pools. Genes involved in signal transduction,the immune response, apoptosis, DNA repair, cell cycle control, and avariety of other physiological functions have clinical relevance andtherapeutic agents that can modulate expression of these genes mayalleviate some or all of the associated symptoms. In some instances,these genes can be described as a member of a family or class of genesand siRNA (randomly, conventionally, or rationally designed) can bedirected against one or multiple members of the family to induce adesired result.

To identify rationally designed siRNA to each gene, the sequence wasanalyzed using Formula VIII or Formula X to identify rationally designedsiRNA. To confirm the activity of these sequences, the siRNA areintroduced into a cell type of choice (e.g., HeLa cells, HEK293 cells)and the levels of the appropriate message are analyzed using one ofseveral art proven techniques. siRNA having heightened levels of potencycan be identified by testing each of the before mentioned duplexes atincreasingly limiting concentrations. Similarly, siRNA having increasedlevels of longevity can be identified by introducing each duplex intocells and testing functionality at 24, 48, 72, 96, 120, 144, 168, and192 hours after transfection. Agents that induce>95% silencing atsub-nanomolar concentrations and/or induce functional levels ofsilencing for >96 hours are considered hyperfunctional.

Example XII Validation of Multigene Knockout Using Rab5 and Eps

Two or more genes having similar, overlapping functions often leads togenetic redundancy. Mutations that knockout only one of, e.g., a pair ofsuch genes (also referred to as homologs) results in little or nophenotype due to the fact that the remaining intact gene is capable offulfilling the role of the disrupted counterpart. To fully understandthe function of such genes in cellular physiology, it is often necessaryto knockout or knockdown both homologs simultaneously. Unfortunately,concomitant knockdown of two or more genes is frequently difficult toachieve in higher organisms (e.g., mice) thus it is necessary tointroduce new technologies dissect gene function. One such approach toknocking down multiple genes simultaneously is by using siRNA. Forexample, FIG. 11 showed that rationally designed siRNA directed againsta number of genes involved in the clathrin-mediated endocytosis pathwayresulted in significant levels of protein reduction (e.g., >80%). Todetermine the effects of gene knockdown on clathrin-related endocytosis,internalization assays were performed using epidermal growth factor andtransferrin. Specifically, mouse receptor-grade EGF (CollaborativeResearch Inc.) and iron-saturated human transferrin (Sigma) wereiodinated as described previously (Jiang, X., Huang, F., Marusyk, A. &Sorkin, A. (2003) Mol Biol Cell 14, 858-70). HeLa cells grown in 12-welldishes were incubated with ¹²⁵I-EGF (1 ng/ml) or ¹²⁵I-transferrin (1μg/ml) in binding medium (DMEM, 0.1% bovine serum albumin) at 37° C.,and the ratio of internalized and surface radioactivity was determinedduring 5-min time course to calculate specific internalization rateconstant k_(e) as described previously (Jiang, X et al.). Themeasurements of the uptakes of radiolabeled transferrin and EGF wereperformed using short time-course assays to avoid influence of therecycling on the uptake kinetics, and using low ligand concentration toavoid saturation of the clathrin-dependent pathway (for EGF Lund, K. A.,Opresko, L. K., Strarbuck, C., Walsh, B. J. & Wiley, H. S. (1990) J.Biol. Chem. 265, 15713-13723).

The effects of knocking down Rab5a, 5b, 5c, Eps, or Eps 15R(individually) are shown in FIG. 22 and demonstrate that disruption ofsingle genes has little or no effect on EGF or Tfn internalization. Incontrast, simultaneous knock down of Rab5a, 5b, and 5c, or Eps and Eps15R, leads to a distinct phenotype (note: total concentration of siRNAin these experiments remained constant with that in experiments in whicha single siRNA was introduced, see FIG. 23). These experimentsdemonstrate the effectiveness of using rationally designed siRNA toknockdown multiple genes and validates the utility of these reagents tooverride genetic redundancy.

Example XIII Validation of Multigene Targeting Using G6PD, GAPDH, PLK,and UQC

Further demonstration of the ability to knock down expression ofmultiple genes using rationally designed siRNA was performed using poolsof siRNA directed against four separate genes. To achieve this, siRNAwere transfected into cells (total siRNA concentration of 100 nM) andassayed twenty-four hours later by B-DNA. Results shown in FIG. 24 showthat pools of rationally designed molecules are capable ofsimultaneously silencing four different genes.

Example XIV Validation of Multigene Knockouts as Demonstrated by GeneExpression Profiling, a Prophetic Example

To further demonstrate the ability to concomitantly knockdown theexpression of multiple gene targets, single siRNA or siRNA poolsdirected against a collection of genes (e.g., 4, 8, 16, or 23 differenttargets) are simultaneously transfected into cells and cultured fortwenty-four hours. Subsequently, mRNA is harvested from treated (anduntreated) cells and labeled with one of two fluorescent probes dyes(e.g., a red fluorescent probe for the treated cells, a greenfluorescent probe for the control cells). Equivalent amounts of labeledRNA from each sample is then mixed together and hybridized to sequencesthat have been linked to a solid support (e.g., a slide, “DNA CHIP”).Following hybridization, the slides are washed and analyzed to assesschanges in the levels of target genes induced by siRNA.

Example XV Identifying Hyperfunctional siRNA

Identification of Hyperfunctional Bcl-2 siRNA

The ten rationally designed Bcl2 siRNA (identified in FIG. 13, 14) weretested to identify hyperpotent reagents. To accomplish this, each of theten Bcl-2 siRNA were individually transfected into cells at a 300 μM(0.3 nM) concentrations. Twenty-four hours later, transcript levels wereassessed by B-DNA assays and compared with relevant controls. As shownin FIG. 25, while the majority of Bcl-2 siRNA failed to inducefunctional levels of silencing at this concentration, siRNA 1 and 8induced>80% silencing, and siRNA 6 exhibited greater than 90% silencingat this subnanomolar concentration.

By way of prophetic examples, similar assays could be performed with anyof the groups of rationally designed genes described in the Examples.Thus for instance, rationally designed siRNA sequences directed againsta gene of interest could be introduced into cells at increasinglylimiting concentrations to determine whether any of the duplexes arehyperfunctional.

Example XVI Gene Silencing: Prophetic Example

Below is an example of how one might transfect a cell.

Select a cell line. The selection of a cell line is usually determinedby the desired application. The most important feature to RNAi is thelevel of expression of the gene of interest. It is highly recommended touse cell lines for which siRNA transfection conditions have beenspecified and validated.

Plate the cells. Approximately 24 hours prior to transfection, plate thecells at the appropriate density so that they will be approximately70-90% confluent, or approximately 1×10⁵ cells/ml at the time oftransfection. Cell densities that are too low may lead to toxicity dueto excess exposure and uptake of transfection reagent-siRNA complexes.Cell densities that are too high may lead to low transfectionefficiencies and little or no silencing. Incubate the cells overnight.Standard incubation conditions for mammalian cells are 37° C. in 5% CO₂.Other cell types, such as insect cells, require different temperaturesand CO₂ concentrations that are readily ascertainable by persons skilledin the art. Use conditions appropriate for the cell type of interest.

siRNA re-suspension. Add 20 μl siRNA universal buffer to each siRNA togenerate a final concentration of 50 μM.

siRNA-lipid complex formation. Use RNase-free solutions and tubes. Usingthe following table, Table XI:

TABLE XI 96-WELL 24-WELL MIXTURE 1 (TRANSIT-TKO-PLASMID DILUTIONMIXTURE) Opti-MEM 9.3 μl  46.5 μl TransIT-TKO (1 μg/μl) 0.5 μl   2.5 μlMIXTURE 1 FINAL VOLUME 10.0 μl   50.0 μl MIXTURE 2 (SIRNA DILUTIONMIXTURE) Opti-MEM 9.0 μl  45.0 μl siRNA (1 μM) 1.0 μl   5.0 μl MIXTURE 2FINAL VOLUME 10.0 μl   50.0 μl MIXTURE 3 (SIRNA-TRANSFECTION REAGENTMIXTURE) Mixture 1 10 μl   50 μl Mixture 2 10 μl   50 μl MIXTURE 3 FINALVOLUME 20 μl  100 μl Incubate 20 minutes at room temperature MIXTURE 4(MEDIA-SIRNA/TRANSFECTION REAGENT MIXTURE) Mixture 3 20 μl  100 μlComplete media 80 μl  400 μl MIXTURE 4 FINAL VOLUME 100 μl   500 μlIncubate 48 hours at 37° C.

Transfection. Create a Mixture 1 by combining the specified amounts ofOPTI-MEM serum free media and transfection reagent in a sterilepolystyrene tube. Create a Mixture 2 by combining specified amounts ofeach siRNA with OPTI-MEM media in sterile 1 ml tubes. Create a Mixture 3by combining specified amounts of Mixture I and Mixture 2. Mix gently(do not vortex) and incubate at room temperature for 20 minutes. Createa Mixture 4 by combining specified amounts of Mixture 3 to completemedia. Add appropriate volume to each cell culture well. Incubate cellswith transfection reagent mixture for 24-72 hours at 37° C. Thisincubation time is flexible. The ratio of silencing will remainconsistent at any point in the time period. Assay for gene silencingusing an appropriate detection method such as RT-PCR, Western blotanalysis, immunohistochemistry, phenotypic analysis, mass spectrometry,fluorescence, radioactive decay, or any other method that is now knownor that comes to be known to persons skilled in the art and that fromreading this disclosure would useful with the present invention. Theoptimal window for observing a knockdown phenotype is related to themRNA turnover of the gene of interest, although 24-72 hours is standard.Final Volume reflects amount needed in each well for the desired cellculture format. When adjusting volumes for a Stock Mix, an additional10% should be used to accommodate variability in pipetting, etc.Duplicate or triplicate assays should be carried out when possible.

Example XVII SiRNAs that Target CFTR

siRNAs that target CFTR [NCBI accession number NM_(—)000492] havingsequences generated in silico by the algorithms herein, are provided. Invarious embodiments, the siRNAs are rationally designed. In variousembodiments, the siRNAs are functional or hyperfunctional. These siRNAthat have been generated by the algorithms of the present inventioninclude:

AAACUUGACUGAACUGGAU; (SEQ. ID NO. 438) AAACUUGGAUCCCUAUGAA; (SEQ. ID NO.439) AAAGAGAAUGGGAUAGAGA; (SEQ. ID NO. 440) AAGAAGAGGUGCAAGAUAC; (SEQ.ID NO. 441) AAGACAAACAGAACUGAAA; (SEQ. ID NO. 442) AAGACUCCCUUACAAAUGA;(SEQ. ID NO. 443) AAGAGGACAUCUCCAAGUU; (SEQ. ID NO. 444)AAGAGGAGACAGAAGAAGA; (SEQ. ID NO. 445) AAGCACAGUGGAAGAAUUU; (SEQ. ID NO.446) AAGCACCAAUCAUGAAUUA; (SEQ. ID NO. 447) AAGUAGUGAUGGAGAAUGU; (SEQ.ID NO. 448) AAUCCUAACUGAGACCUUA; (SEQ. ID NO. 449) AAUGAUAACUGGAAACUUC;(SEQ. ID NO. 450) AAUUAAGCACAGUGGAAGA; (SEQ. ID NO. 451)AAUUGUGAUUGGAGCUAUA; (SEQ. ID NO. 452) ACAAAGCUCUGAAUUUACA; (SEQ. ID NO.453) ACAAAUGAAUGGCAUCGAA; (SEQ. ID NO. 454) ACAAUAUAGUUCUUGGAGA; (SEQ.ID NO. 455) ACACAUACCUUCGAUAUAU; (SEQ. ID NO. 456) ACACUGCGCUGGUUCCAAA;(SEQ. ID NO. 457) ACGAAGAAGACUUAAAGGA; (SEQ. ID NO. 458)ACUCCUGUCCUGAAAGAUA; (SEQ. ID NO. 459) ACUGAACACUGAAGGAGAA; (SEQ. ID NO.460) AGAAACUGGCUUGGAAAUA; (SEQ. ID NO. 461) AGAAGGAAGAGUUGGUAUU; (SEQ.ID NO. 462) AGAAGGUUAUCUCAAGAAA; (SEQ. ID NO. 463) AGACAGAAGAAGAGGUGCA;(SEQ. ID NO. 464) AGAGCUGGCUUCAAAGAAA; (SEQ. ID NO. 465)AGAGGUGGCUGCUUCUUUG; (SEQ. ID NO. 466) AGGAGAAGGAGAAGGAAGA; (SEQ. ID NO.467) AGGGAAGACUACUUUGUUA; (SEQ. ID NO. 468) AGGUGGAAAUGCCAUAUUA; (SEQ.ID NO. 469) AUAGAGAGCUGGCUUCAAA; (SEQ. ID NO. 470) AUGCCAUAUUAGAGAACAU;(SEQ. ID NO. 471) CAAACUGACUCUUAAGAAG; (SEQ. ID NO. 472)CAAAGCAUGCCAACUAGAA; (SEQ. ID NO. 473) CAAAUUUGAUGAAGGACUU; (SEQ. ID NO.474) CAACAACCUGAACAAAUUU; (SEQ. ID NO. 475) CAACAGUGCCAGUGAUAGU; (SEQ.ID NO. 476) CAACCAAACCAUACAAGAA; (SEQ. ID NO. 477) CAAGAAAUAUGGAAAGUUG;(SEQ. ID NO. 478) CAAGAAGGUUAUCUCAAGA; (SEQ. ID NO. 479)CAAGACAAAGGGAAUAGUA; (SEQ. ID NO. 480) CAAGAGCAGUAUACAAAGA; (SEQ. ID NO.481) CAAGCAAGUGCAAGUCUAA; (SEQ. ID NO. 482) CAAUCAACUCUAUACGAAA; (SEQ.ID NO. 483) CAGAAGAAGAGGUGCAAGA; (SEQ. ID NO. 484) CAGAAGUAGUGAUGGAGAA;(SEQ. ID NO. 485) CAGCCAGACUUUAGCUCAA; (SEQ. ID NO. 486)CAGGUGGGAUUCUUAAUAG; (SEQ. ID NO. 487) CAGUGAAAGACUUGUGAUU; (SEQ. ID NO.488) CAUCAAAGCAUGCCAACUA; (SEQ. ID NO. 489) CCAAAUCCCUUCUGUUGAU; (SEQ.ID NO. 490) CCAAAUGAGAAUAGAAAUG; (SEQ. ID NO. 491) CCAACAACCUGAACAAAUU;(SEQ. ID NO. 492) CCAACUAUCUCAUUUCCAA; (SEQ. ID NO. 493)CCAAGUCAACCAAACCAUA; (SEQ. ID NO. 494) CCAAGUUUGCAGAGAAAGA; (SEQ. ID NO.495) CCAAUCAACUCUAUACGAA; (SEQ. ID NO. 496) CCACAAAGCUCUGAAUUUA; (SEQ.ID NO. 497) CCACAAGACUGCACAUCAA; (SEQ. ID NO. 498) CCAGUAACAUACCAAAUAA;(SEQ. ID NO. 499) CCUAUGACCCCGAUAACAA; (SEQ. ID NO. 500)CCUCAGAAAUGAUUGAAAA; (SEQ. ID NO. 501) CCUCCAAACCUCACAGCAA; (SEQ. ID NO.502) CCUUACAAAUGAAUGGCAU; (SEQ. ID NO. 503) CCUUCUGGGAGGAGGGAUU; (SEQ.ID NO. 504) CUAGAUCUGUUCUCAGUAA; (SEQ. ID NO. 505) CUCCAAAGAUAUAGCAAUU;(SEQ. ID NO. 506) CUCCAAGUUUGCAGAGAAA; (SEQ. ID NO. 507)CUUAAUAGAUUCUCCAAAG; (SEQ. ID NO. 508) CUUCAAGACAAAGGGAAUA; (SEQ. ID NO.509) CUUGGAAAUAAGUGAAGAA; (SEQ. ID NO. 510) GAAACUCGUUAAUUUGUAG; (SEQ.ID NO. 511) GAAACUCUGUUCCACAAAG; (SEQ. ID NO. 512) GAAACUGGCUUGGAAAUAA;(SEQ. ID NO. 513) GAAAGAGAAUGGGAUAGAG; (SEQ. ID NO. 514)GAAAGAGGAGACAGAAGAA; (SEQ. ID NO. 515) GAAAGCAGGUGGGAUUCUU; (SEQ. ID NO.516) GAAAGGCAGCUCUAAAUGU; (SEQ. ID NO. 517) GAAAGUUGCAGAUGAGGUU; (SEQ.ID NO. 518) GAAAUCAGGGUUAGUAUUG; (SEQ. ID NO. 519) GAAAUGCCAUAUUAGAGAA;(SEQ. ID NO. 520) GAAAUUCAAUCCUAACUGA; (SEQ. ID NO. 521)GAACAAAUUUGAUGAAGGA; (SEQ. ID NO. 522) GAACACAGGAUAGAAGCAA; (SEQ. ID NO.523) GAACACAUACCUUCGAUAU; (SEQ. ID NO. 524) GAACAUUCACCGAAAGACA; (SEQ.ID NO. 525) GAACAUUUCCUUCUCAAUA; (SEQ. ID NO. 526) GAACCUAGGGUGAUAUUAA;(SEQ. ID NO. 527) GAACUGAAAUCAUACUUCU; (SEQ. ID NO. 528)GAAGAAGACUUAAAGGAGU; (SEQ. ID NO. 529) GAAGAAGAGGUGCAAGAUA; (SEQ. ID NO.530) GAAGAAGCACCAAUCAUGA; (SEQ. ID NO. 531) GAAGAGGACAUCUCCAAGU; (SEQ.ID NO. 532) GAAGAGUACUUUGUUAUCA; (SEQ. ID NO. 533) GAAGAUCAGUGAAAGACUU;(SEQ. ID NO. 534) GAAGGAAGAGUUGGUAUUA; (SEQ. ID NO. 535)GAAGGAGAAGGAAGAGUUG; (SEQ. ID NO. 536) GAAGGCAGGAGUCCAAUUU; (SEQ. ID NO.537) GAAGGUGGAAUCACACUGA; (SEQ. ID NO. 538) GAAUUGAGGACACUGAUAU; (SEQ.ID NO. 539) GACAGCCUCUUCUUCAGUA; (SEQ. ID NO. 540) GACAUUUGCUCAUGGAAUU;(SEQ. ID NO. 541) GAGAAAGACAAUAUAGUUC; (SEQ. ID NO. 542)GAGAAGAACUGAAAUCAUA; (SEQ. ID NO. 543) GAGAAGGAAGAGUUGGUAU; (SEQ. ID NO.544) GAGAAUAGCUAUGUUUAGU; (SEQ. ID NO. 545) GAGAGCAGCAUAAAUGUUG; (SEQ.ID NO. 546) GAGAGCAUAUUUCCUCCAA; (SEQ. ID NO. 547) GAGCAUACCAGCAGUGACU;(SEQ. ID NO. 548) GAGCAUAUUUCCUCCAAAC; (SEQ. ID NO. 549)GAGGACAUCUCCAAGUUUG; (SEQ. ID NO. 550) GAGUUUAGCUGGAAAAGUA; (SEQ. ID NO.551) GAUAGAAAGAGGACAGUUG; (SEQ. ID NO. 552) GAUAGUGGCUUUUAUUAUG; (SEQ.ID NO. 553) GAUCAAGAAAUAUGGAAAG; (SEQ. ID NO. 554) GAUCAGGGAAGAGUACUUU;(SEQ. ID NO. 555) GAUGACAGCCUCUUCUUCA; (SEQ. ID NO. 556)GAUGAGAAUAGCUAUGUUU; (SEQ. ID NO. 557) GAUGGUGUGUCUUGGGAUU; (SEQ. ID NO.558) GAUUGGAGCUAUAGCAGUU; (SEQ. ID NO. 559) GCAAAAGACUCCCUUACAA; (SEQ.ID NO. 560) GCAAGAAUAUAAGACAUUG; (SEQ. ID NO. 561) GCACAGUGGAAGAAUUUCA;(SEQ. ID NO. 562) GCAGAAAGAAGAAAUUCAA; (SEQ. ID NO. 563)GCAGAGAAAGACAAUAUAG; (SEQ. ID NO. 564) GCAGGUGGGAUUCUUAAUA; (SEQ. ID NO.565) GCAUAGAUGUGGAUAGCUU; (SEQ. ID NO. 566) GCAUUUAGAUGUAUAGGUU; (SEQ.ID NO. 567) GCCCACAGCUGUAUGAUUC; (SEQ. ID NO. 568) GCUAACAAAACUAGGAUUU;(SEQ. ID NO. 569) GCUCAAAACUCAUGGGAUG; (SEQ. ID NO. 570)GCUCAGAUCUGUGAUAGAA; (SEQ. ID NO. 571) GCUGGAAUGCCAACAAUUU; (SEQ. ID NO.572) GCUGGGAAGAUCAGUGAAA; (SEQ. ID NO. 573) GCUUAUGCCUUCUCUUUAU; (SEQ.ID NO. 574) GGAAAGAGAAUGGGAUAGA; (SEQ. ID NO. 575) GGAAAGGCAGCUCUAAAUG;(SEQ. ID NO. 576) GGAAAGGGCUGUUAUAAUC; (SEQ. ID NO. 577)GGAAAUGCCAUAUUAGAGA; (SEQ. ID NO. 578) GGAACGCUCUAUCGCGAUU; (SEQ. ID NO.579) GGAAGCAAAUGACAUCACA; (SEQ. ID NO. 580) GGAAGUCACCAAAGCAGUA; (SEQ.ID NO. 581) GGAAUUAUUUGAGAAAGCA; (SEQ. ID NO. 582) GGACUUGCAUUGGCACAUU;(SEQ. ID NO. 583) GGACUUGGUUUCCUGAUAG; (SEQ. ID NO. 584)GGAGAAUGAUGAUGAAGUA; (SEQ. ID NO. 585) GGAGAUUUAUGUUCUAUGG; (SEQ. ID NO.586) GGAGUGAUCAAGAAAUAUG; (SEQ. ID NO. 587) GGAUCAGGGAAGAGUACUU; (SEQ.ID NO. 588) GGCAAGACUUCACUUCUAA; (SEQ. ID NO. 589) GGGAAGAAGCAAUGGAAAA;(SEQ. ID NO. 590) GGGAAGAGUACUUUGUUAU; (SEQ. ID NO. 591)GGGAAUUAUUUGAGAAAGC; (SEQ. ID NO. 592) GGGAGAACCUAGGGUGAUA; (SEQ. ID NO.593) GGGAGAAUGAUGAUGAAGU; (SEQ. ID NO. 594) GGGAGUAGCCGACACUUUG; (SEQ.ID NO. 595) GGGCUAGGGAGAAUGAUGA; (SEQ. ID NO. 596) GGUAGAGGGUGUGUAAGUA;(SEQ. ID NO. 597) GGUAUUAUCCUGACUUUAG; (SEQ. ID NO. 598)GGUCAACGAGCAAGAAUUU; (SEQ. ID NO. 599) GGUCAUAGAAGAGAACAAA; (SEQ. ID NO.600) GGUGCAAGAUACAAGGCUU; (SEQ. ID NO. 601) GUAAACUGAUGGCUAACAA; (SEQ.ID NO. 602) GUACAAACAUGGUAUGACU; (SEQ. ID NO. 603) GUACUCCUGUCCUGAAAGA;(SEQ. ID NO. 604) GUACUUUGUUAUCAGCUUU; (SEQ. ID NO. 605)GUCAGAACAUUCACCGAAA; (SEQ. ID NO. 606) GUGAAAGACUUGUGAUUAC; (SEQ. ID NO.607) GUGAAGAAAUUAACGAAGA; (SEQ. ID NO. 608) GUGAUUACCUCAGAAAUGA; (SEQ.ID NO. 609) GUGCAAGAUACAAGGCUUU; (SEQ. ID NO. 610) GUGGAGUGAUCAAGAAAUA;(SEQ. ID NO. 611) GUGGAUAGCUUGAUGCGAU; (SEQ. ID NO. 612)UAACAGCUAUGCAGUGAUU; (SEQ. ID NO. 613) UAACCAAGGUCAGAACAUU; (SEQ. ID NO.614) UAACGAAGAAGACUUAAAG; (SEQ. ID NO. 615) UAAGCACAGUGGAAGAAUU; (SEQ.ID NO. 616) UAUGGAAAGUUGCAGAUGA; (SEQ. ID NO. 617) UCAAACAACUGGAAUCUGA;(SEQ. ID NO. 618) UCAAAGAUCUCACAGCAAA; (SEQ. ID NO. 619)UCAAGAAGGUUAUCUCAAG; (SEQ. ID NO. 620) UCACAGACCUUUGAACUAG; (SEQ. ID NO.621) UCAGAGAGCUGGGAAGAUC; (SEQ. ID NO. 622) UCUGUAAACUGAUGGCUAA; (SEQ.ID NO. 623) UCUUGGAGCAAUAAACAAA; (SEQ. ID NO. 624) UGAAAGAGGAGACAGAAGA;(SEQ. ID NO. 625) UGAAAGCUGUGUCUGUAAA; (SEQ. ID NO. 626)UGACUCUCUUGGAGCAAUA; (SEQ. ID NO. 627) UGACUGAACUGGAUAUAUA; (SEQ. ID NO.628) UGACUUUAGCCAUGAAUAU; (SEQ. ID NO. 629) UGAUAUGGGUCUUGAUAAA; (SEQ.ID NO. 630) UGAUGAGCCUUUAGAGAGA; (SEQ. ID NO. 631) UGGGAAGAAGCAAUGGAAA;(SEQ. ID NO. 632) UGGGAAGAUCAGUGAAAGA; (SEQ. ID NO. 633)UGGUCAUAGAAGAGAACAA; (SEQ. ID NO. 634) UUACAACCCUACAUCUUUG; (SEQ. ID NO.635) UUAUCUAGGCAUAGGCUUA; (SEQ. ID NO. 636) UUGAUGAUAUGGAGAGCAU; (SEQ.ID NO. 637) UUGCAGAGAAAGACAAUAU; (SEQ. ID NO. 638) UUGGAAAGAGAAUGGGAUA;(SEQ. ID NO. 639) and UUGGAAUGCAGAUGAGAAU. (SEQ. ID NO. 640)

Thus, consistent with Example XVII, the present invention provides ansiRNA that targets CFTR provided, wherein the siRNA is selected from thegroup consisting of SEQ. ID NOs. 438-640.

In another embodiment, an siRNA is provided, said siRNA comprising asense region and an antisense region, wherein said sense region and saidantisense region are at least 90% complementary, said sense region andsaid antisense region together form a duplex region comprising 18-30base pairs, and said sense region comprises a sequence that is at least90% similar to a sequence selected from the group consisting of: SEQ. IDNOs 438-640.

In another embodiment, an siRNA is provided wherein the siRNA comprisesa sense region and an antisense region, wherein said sense region andsaid antisense region are at least 90% complementary, said sense regionand said antisense region together form a duplex region comprising 18-30base pairs, and said sense region comprises a sequence that is identicalto a contiguous stretch of at least 18 bases of a sequence selected fromthe group consisting of: SEQ. ID NOs 438-640.

In another embodiment, an siRNA is provided wherein the siRNA comprisesa sense region and an antisense region, wherein said sense region andsaid antisense region are at least 90% complementary, said sense regionand said antisense region together form a duplex region comprising 19-30base pairs, and said sense region comprises a sequence that is identicalto a contiguous stretch of at least 18 bases of a sequence selected fromthe group consisting of: SEQ. ID NOs 438-640.

In another embodiment, a pool of at least two siRNAs is provided,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprises a duplex region of length 18-30 base pairs that has afirst sense region that is at least 90% similar to 18 bases of a firstsequence selected from the group consisting of: SEQ. ID NOs 438-640 andsaid second siRNA comprises a duplex region of length 18-30 base pairsthat has a second sense region that is at least 90% similar to 18 basesof a second sequence selected from the group consisting of: SEQ. ID NOs438-640 and wherein said first sense region and said second sense regionare not identical.

In another embodiment, a pool of at least two siRNAs is provided,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprises a duplex region of length 18-30 base pairs that has afirst sense region that is identical to at least 18 bases of a sequenceselected from the group consisting of: SEQ. ID NOs 438-640 and whereinthe second siRNA comprises a second sense region that comprises asequence that is identical to at least 18 bases of a sequence selectedfrom the group consisting of: SEQ. ID NOs 438-640.

In another embodiment, a pool of at least two siRNAs is provided,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprises a duplex region of length 19-30 base pairs and has afirst sense region comprising a sequence that is at least 90% similar toa sequence selected from the group consisting of: SEQ. ID NOs 438-640,and said duplex of said second siRNA is 19-30 base pairs and comprises asecond sense region that comprises a sequence that is at least 90%similar to a sequence selected from the group consisting of: SEQ. ID NOs438-640.

In another embodiment, a pool of at least two siRNAs is provided,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprises a duplex region of length 19-30 base pairs and has afirst sense region comprising a sequence that is identical to at least18 bases of a sequence selected the group consisting of: SEQ. ID NOs438-640 and said duplex of said second siRNA is 19-30 base pairs andcomprises a second sense region comprising a sequence that is identicalto a sequence selected from the group consisting of: SEQ. ID NOs438-640.

While the invention has been described in connection with specificembodiments thereof, it will be understood that it is capable of furthermodifications and this application is intended to cover any variations,uses, or adaptations of the invention following, in general, theprinciples of the invention and including such departure from thepresent disclosure as come within known or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth and as follows in the scope ofthe appended claims.

1. An siRNA comprising a sense region and an antisense region, whereinsaid sense region and said antisense region together form a duplexregion, said antisense region and said sense region are each 18-30nucleotides in length and said antisense region comprises a sequencethat is at least 90% complementary to a sequence selected from the groupconsisting of SEQ. ID NOs. 438-640.
 2. An siRNA comprising a senseregion and an antisense region, wherein said sense region and saidantisense region together form a duplex region and said sense region andsaid antisense region are each 18-30 nucleotides in length, and saidantisense region comprises a sequence that is 100% complementary to acontiguous stretch of at least 18 bases of a sequence selected from thegroup consisting of SEQ. ID NOs. 438-640.
 3. The siRNA of claim 2,wherein each of said antisense region and said sense region are 19-30nucleotides in length, and said antisense region comprises a sequencethat is 100% complementary to said sequence selected from the groupconsisting of: SEQ. ID NOs. 438-640.
 4. A pool of at least two siRNAs,wherein said pool comprises a first siRNA and a second siRNA, said firstsiRNA comprises a first antisense region and a first sense region thattogether form a first duplex region and each of said first antisenseregion and said first sense region are 18-30 nucleotides in length andsaid first antisense region is at least 90% complementary to 18 bases ofa first sequence selected from the group consisting of: SEQ. ID NOs.438-640 and said second siRNA comprises a second antisense region and asecond sense region that together form a second duplex region and eachof said second antisense region and said second sense region are 18-30nucleotides in length and said second antisense region is at least 90%complementary to 18 bases of a second sequence selected from the groupconsisting of: SEQ. ID NOs. 438-640, wherein said first antisense regionand said second antisense region are not identical.
 5. The pool of claim4, wherein said first antisense region comprises a sequence that is 100%complementary to at least 18 bases of said first sequence, and saidsecond antisense region comprises a sequence that is 100% complementaryto at least 18 bases of said second sequence.
 6. The pool of claim 4,wherein said first siRNA is 19-30 nucleotides in length and said firstantisense region comprises a sequence that is at least 90% complementaryto said first sequence, and second siRNA is 19-30 nucleotides in lengthand said second antisense region comprises a sequence that is at least90% complementary to said second sequence.
 7. The pool of claim 4,wherein said first antisense region is 19-30 nucleotides in length andsaid first antisense region comprises a sequence that is 100%complementary to at least 18 bases of said first sequence, and saidsecond antisense region is 19-30 nucleotides in length and said secondantisense region comprises a sequence that is 100% complementary to saidsecond sequence.
 8. The siRNA of claim 1, wherein said antisense regionand said sense region are each 19-25 nucleotides in length.
 9. The siRNAof claim 4, wherein said first antisense region, said first senseregion, said second sense region and said second antisense region areeach 19-25 nucleotides in length.